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Why is that? I would think I would use both, it seems much more efficient. Have I always only been breathing out of my right nostril?","confidence":0.98,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"breathe_out_of_one_nostril/Nostril_0_1.txt","reverseDid":"breathe_out_of_one_nostril/Nostril_0_1.txt","forwardChoice":"breathe_out_of_one_nostril/Nostril_0_1.txt","reverseChoice":"breathe_out_of_one_nostril/Nostril_0_1.txt"},{"dataset":"bright-biology","qid":"10","query":"If the brain has no pain receptors, how come you can get a headache?\nI've read many years ago in books, that the brain has no nerves on it, and if someone was touching your brain, you couldn't feel a thing.\nJust two days before now, I had a very bad migraine, due to a cold. It's become better now, but when I had it I felt my head was going to literally split in half, as the pain was literally coming from my brain.\nSo it lead me to the question: How come people can get headaches if the brain has no nerves?","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"brain_no_pain_receptors_headache/Headache_2.txt","reverseDid":"brain_no_pain_receptors_headache/Headache_2.txt","forwardChoice":"brain_no_pain_receptors_headache/Headache_2.txt","reverseChoice":"brain_no_pain_receptors_headache/Headache_2.txt"},{"dataset":"bright-biology","qid":"100","query":"What types of light can't a plant photosynthesize in?\nI have a plant on my desk, and it got me to wondering: Can my plant use the light from my monitors to photosynthesize? If so, what light (apart from green light, to a degree) can't plants use to perform photosynthesis?\nI know that plants have the photosynthetic pigments to absorb many different wavelengths of light (primarily red and blue) but would there be certain types of light it can't use?\n(The specific plant by the way is Schlumbergera truncata)","confidence":0.79,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"plant_light_pho/Chlorophyll_0.txt","reverseDid":"plant_light_pho/Chlorophyll_0.txt","forwardChoice":"plant_light_pho/Chlorophyll_0.txt","reverseChoice":"plant_light_pho/Chlorophyll_0.txt"},{"dataset":"bright-biology","qid":"11","query":"Why does a coconut have exactly three \"holes\"?\nWhy do coconuts have exactly three \"holes\"","confidence":0.87,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"coconut_three_holes/Coconut_2.txt","reverseDid":"coconut_three_holes/Coconut_1_0.txt","forwardChoice":"coconut_three_holes/Coconut_2.txt","reverseChoice":"coconut_three_holes/Coconut_1_0.txt"},{"dataset":"bright-biology","qid":"13","query":"How do trees manage to grow equally in all directions?\nI was walking down a road with these beautifully huge trees when this question occurred to me.\nLarge trees with many thick branches have to grow equally in all directions, or they would tip over. Is there some sort of mechanism to ensure this uniform growth? Or is it just a happy coincidence arising from uniform availability of sunlight on all sides? If one branch of a tree becomes too heavy due to many sub-branches, does this somehow trigger growth on the opposite side of the tree?\nI have seen that potted plants in houses tend to grow towards sunlight. My mum often turns pots around by 180 degrees to ensure that plants don't bend in any one direction. I assume that this is because sunlight increases the rate of photosynthesis, leading to rapid growth of the meristem. For trees growing in open spaces, this wouldn't be a problem. But there are many large trees that grow in the shadow of buildings without bending away from the buildings, even though this is the only direction from which they would receive any sunlight. Is there any explanation for this?","confidence":0.5,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"trees_grow_directions/Gravitropism_2.txt","reverseDid":"trees_grow_directions/Gravitropism_2.txt","forwardChoice":"trees_grow_directions/Gravitropism_2.txt","reverseChoice":"trees_grow_directions/Gravitropism_2.txt"},{"dataset":"bright-biology","qid":"17","query":"What is 'protein' in food?\nI know that proteins perform a lot of functions and as a result there are a lot of different types of proteins in our bodies. When I eat food that has x grams of 'protein', what is this? A homogenous mix of proteins? A heterogenous one? Is it specific set of them that are general enough to be used by my body despite coming from a different organism? How can it be sure that my body can use the kind of protein in the food?\n","confidence":0.65,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"protein_in_food/Protein_(nutrient)_4.txt","reverseDid":"protein_in_food/Protein_(nutrient)_4.txt","forwardChoice":"protein_in_food/Protein_(nutrient)_4.txt","reverseChoice":"protein_in_food/Protein_(nutrient)_4.txt"},{"dataset":"bright-biology","qid":"19","query":"Is kissing a natural human activity?\nThe word natural here is meant in contrast to it being a sociological construct.\nIs kissing in all its forms something natural for humans? Is it instinctively erotic? Or is it just a conventional form to show trust and intimicy, i.e. the association besically just comes via a social means?\nBecause the only other advantage of this mouth to mouth contact I could see is maybe for the immune system.","confidence":0.56,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"kissing_natural_human_activity/Kiss_8.txt","reverseDid":"kissing_natural_human_activity/Kiss_8.txt","forwardChoice":"kissing_natural_human_activity/Kiss_8.txt","reverseChoice":"kissing_natural_human_activity/Kiss_8.txt"},{"dataset":"bright-biology","qid":"20","query":"Which organism has the smallest genome length?\nWhich animal/plant/anything has smallest length genome?","confidence":0.56,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"genetic_sequence_of_SARS-CoV-2/Genome_7_3.txt","reverseDid":"genetic_sequence_of_SARS-CoV-2/Genome_7_3.txt","forwardChoice":"genetic_sequence_of_SARS-CoV-2/Genome_7_3.txt","reverseChoice":"genetic_sequence_of_SARS-CoV-2/Genome_7_3.txt"},{"dataset":"bright-biology","qid":"21","query":"Do immortal organisms exist?\nDo organisms exist that are able to live indefinitely if they are not killed by external factors?\nUnder external factors I would consider things like predators and natural disaster but not illness by bacteria for example.","confidence":0.59,"flipped":true,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"die_if_cannot_breathe/Death_10_4.txt","reverseDid":"die_if_cannot_breathe/Death_0_0.txt","forwardChoice":"die_if_cannot_breathe/Death_10_4.txt","reverseChoice":"die_if_cannot_breathe/Death_0_0.txt"},{"dataset":"bright-biology","qid":"27","query":"Can we taste electrons?\nWhy does licking a 9-Volt battery elicits a taste sensation? Can taste be stimulated by electrical stimulation?","confidence":0.57,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"electrical_shock_freeze_up_muscles/Electrical_injury_2_7.txt","reverseDid":"electrical_shock_freeze_up_muscles/Electrical_injury_2_7.txt","forwardChoice":"none","reverseChoice":"none"},{"dataset":"bright-biology","qid":"28","query":"Hot water and bacteria\nI know that it is common to say, \"use hot water when washing your hands\" or when you've got a cut, \"wash your hands with warm water,\" etc. I was wondering, why is this the case? Since bacteria grow in warm environments, you would think that it might be beneficial for bacteria. Is this just another myth or is this actually true?","confidence":0.93,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"hot_water_bacteria/Hand_washing_0_13.txt","reverseDid":"hot_water_bacteria/Hand_washing_0_13.txt","forwardChoice":"hot_water_bacteria/Hand_washing_0_13.txt","reverseChoice":"hot_water_bacteria/Hand_washing_0_13.txt"},{"dataset":"bright-biology","qid":"29","query":"Why does my room suddenly look 'reddish'? My eyes seem to adapt to color\nTo get the context of this question clear, I would like you to walk through some parts of my house.\nWe'll start with one of my rooms as it appears normally - area Y\nAs evident, this part of my house has a creamish tinge to it, also the balcony door is open which further gives this room a yellow tint. Nothing special. I'll call it \"area Y\" (for yellow)*. Let's move on.\narea G\nHere we arrive in another part of my house which has greenish/blue shades acting as a sunlight blocker. This gives this entire place a greenish/blue tint as shown. (Ref. \"area G\")\nSo, now let's visit the area Y again. I am always surprised with what my eyes now see. This. {1}\nenter image description here\nYou see, the same room, same lightning, yet the room now looks much more reddish than before! That's what intrigues me, why does it happen? Why my eyes suddenly become sensitive to red color?\nI am not a biology student but I do know that our eyes have cones which specialize in color perception, and that there are different types of cone cells to perceive different types of colors (If I remember my high school biology correctly).\nSo, I somewhat get a feeling of what's happening inside my eyes (or the brain perhaps?). I suspect that as I go to area G, my eyes 'adapt' to the dominance of bluish/green light, either by decreasing pigment of blue cones or by increasing pigment of red cones, which makes my eyes more sensitive to the other end of visible light spectrum (red-yellow), causing an 'outburst' of red coloration in area 'Y'. Since pigments need time to degrade, by the time the red color pigment of my cones decreases, its activity is less than normal. It may be increased as the blue pigment's activity is less than normal and I see a reddish tinge for few moments.\nThis was a pure hypothesis from my part, but it seems correct to me. But why do my eyes even adapt to blue color? Why can't my eyes be unbiased towards colors?\n{1} Obviously I photoshopped the red tint in the image how else could I have replicated my vision? Though it's not the most accurate description of what I see it's still a very close approximation.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"reddish_adapt_to_color/Afterimage_1.txt","reverseDid":"reddish_adapt_to_color/Afterimage_1.txt","forwardChoice":"reddish_adapt_to_color/Afterimage_1.txt","reverseChoice":"reddish_adapt_to_color/Afterimage_1.txt"},{"dataset":"bright-biology","qid":"3","query":"Do animals exhibit handedness (paw-ness?) preference?\nI have been observing my cat and found that when confronted with an unknown item, she will always use her front left paw to touch it.\nThis has me wondering if animals exhibit handedness like humans do? (and do I have a left handed cat?)\nOne note of importance is that with an unknown item, her approach is always identical, so possibly using the left paw means allowing a fast possible exit based on how she positions her body.\nThis question is related to Are there dextral/sinistral higher animals?. However, I question the \"paw-ness\" as a consequence of how the cat is approaching new items (to be ready to flee), whereas the other question remarks about the high number of \"right-pawed\" dogs and questions the influence of people for this preference.","confidence":0.47,"flipped":true,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"animals_handedness/Laterality_2.txt","reverseDid":"animals_handedness/Handedness_7.txt","forwardChoice":"animals_handedness/Laterality_2.txt","reverseChoice":"animals_handedness/Handedness_7.txt"},{"dataset":"bright-biology","qid":"30","query":"Why do adults lose hearing at high frequencies?\nChildren and teens can hear high pitched sounds that adults can't hear anymore. Why do adults lose high-frequency hearing?","confidence":0.36,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"adults_lose_hearing/Presbycusis_1.txt","reverseDid":"fake_auditory_signal/auditory-pathway_50_0.txt","forwardChoice":"adults_lose_hearing/Presbycusis_1.txt","reverseChoice":"fake_auditory_signal/auditory-pathway_50_0.txt"},{"dataset":"bright-biology","qid":"32","query":"Why can't humans drink sea water?\nIt would seem to be a huge evolutionary advantage for an animal to be able to drink sea water rather than have to rely on fresh water, and it's provably not impossible due to the existence of sea mammals that must filter the sea water somehow.\nCould someone explain to me any likely reasons that this is the case?","confidence":0.7,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"drink_sea_water/osmolarity_of_sea_water_0.txt","reverseDid":"fiber_eating_birds/VM067_15_0.txt","forwardChoice":"none","reverseChoice":"none"},{"dataset":"bright-biology","qid":"4","query":"What is the evolutionary advantage of red-green color blindness?\nRed-green colorblindness seems to make it harder for a hunter-gatherer to see whether a fruit is ripe and thus worth picking.\nIs there a reason why selection hasn't completely removed red-green color blindness? Are there circumstances where this trait provides an evolutionary benefit?","confidence":1.0,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"evolutionary_advantage_of_red-green_color_blindness/Color_blindness_1_12.txt","reverseDid":"evolutionary_advantage_of_red-green_color_blindness/Color_blindness_1_12.txt","forwardChoice":"evolutionary_advantage_of_red-green_color_blindness/Color_blindness_1_12.txt","reverseChoice":"evolutionary_advantage_of_red-green_color_blindness/Color_blindness_1_12.txt"},{"dataset":"bright-biology","qid":"5","query":"Can a woman give birth to twins with different fathers?\nIs it possible for a woman to conceive from two different men and give birth to half-siblings?","confidence":0.85,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"twins_with_different_fathers/Superfecundation_0.txt","reverseDid":"twins_with_different_fathers/Superfecundation_0.txt","forwardChoice":"twins_with_different_fathers/Superfecundation_0.txt","reverseChoice":"twins_with_different_fathers/Superfecundation_0.txt"},{"dataset":"bright-biology","qid":"52","query":"Are two eyes necessary for 3D vision?\nIt has always been my understanding that humans have two eyes so that we can have 3D vision: the left eye sees more of the left side of an object than the right eye and vice versa. This helps us to estimate depth among other things.\nNow when I close one eye, I still am able to perceive depth. I assume this is because my brain fills in the blanks For how long does this work? Do people eventually lose the depth perception (or at least it diminishes significantly) when they lose a single eye?\nIf so, how low does it take? If not, clearly we are capable of perceiving everything with one eye. Why do we then have two (besides redundancy and a larger field of view)? What is considering the evolution of man better in having two eyes as opposed to one or three, or four,..?","confidence":0.55,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"3D_vision/full_61_0.txt","reverseDid":"3D_vision/full_61_0.txt","forwardChoice":"3D_vision/full_61_0.txt","reverseChoice":"3D_vision/full_61_0.txt"},{"dataset":"bright-biology","qid":"57","query":"Why do dog's eyes glow green on a photo?\nTaking my current profile picture as a prominent example:\nDog's eyes glowing green in camera\nThis photo has been taken with flash. As I gather, this is the same effect that makes a human's eyes glow red on photos, namely the reflection of the flash at the choroid and then passing back through the retina.\nThe retina is full of bloodvessels and the blood inside them gives the reflected light it's reddish color.\nIf it's the same effect, why is the color different?","confidence":0.72,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"dog_eyes_green/Tapetum_lucidum_3.txt","reverseDid":"dog_eyes_glow/Tapetum_lucidum_3.txt","forwardChoice":"dog_eyes_green/Tapetum_lucidum_3.txt","reverseChoice":"dog_eyes_glow/Tapetum_lucidum_3.txt"},{"dataset":"bright-biology","qid":"58","query":"How to make a fake auditory signal?\nMy question is about making fake auditory signals. The ear collects sounds from the environment, which are transformed into a neural signal by the hair cells in the inner ear. This signal is sent through the auditory nerve to the to brain, where it is decoded into a hearing sensation.\nNow can we make fake signals and feed them into the auditory nerve to bring it to the brain? E.g., instead of the ear collecting sound, could we use an electrical instrument for collecting sound and convert this signal into a neural signal in the auditory nerve?","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"fake_auditory_signal/Cochlear_implant_2.txt","reverseDid":"fake_auditory_signal/Cochlear_implant_2.txt","forwardChoice":"fake_auditory_signal/Cochlear_implant_2.txt","reverseChoice":"fake_auditory_signal/Cochlear_implant_2.txt"},{"dataset":"bright-biology","qid":"62","query":"How does a neuron change as you learn?\nI am currently taking a course called \"Introduction to Machine Learning with ENCOG 3\", and I have a question about how well the Artificial Intelligence (AI) algorithm for a \"neural network\" corresponds with how an actual neuron works.\nIn the course, they model a neuron like this: AI implementation of a neuron's algorithm\nx1, x2, etc. are voltage inputs, the wij are weights. The inputs are multiplied by these weights and summed up by the neuron. The neuron then has an \"activation function\" which then takes the sum of weighted inputs and calculates an output, typically a value between 0 and 1 (or between -1 and 1). You can think of the wij as representing dendrites (a higher weight means a more dense and thus conductive dendrite), and the output of the activation function as the voltage that gets sent down an axon.\nThe AI neural network algorithm creates a kind of intelligence by modifying the weights (wij shown in the picture).\nMy first questions is: Is this a good approximation as to how neurons actually work? That is, do our neurons \"learn\" by changing the weights (dendrite density, conductivity)? Or, is there some other mechanism that is more important (e.g. do neurons learn by changing their activation or summation functions?)\nMy second question is: If neurons really do learn by changing the density of dendrites, then how fast does this happen? Is this a fast process like DNA replication? Does the neuron quickly generate (or decrease) dendrite density when it receives some kind of biochemical signal that it needs to learn now?\nI understand that much of this might not yet be known, but would like to know how well the AI algorithm corresponds with current theories on biological neural networks.","confidence":0.43,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"neuron_onto/thesynapse_166_0.txt","reverseDid":"neuron_onto/thesynapse_166_0.txt","forwardChoice":"neuron_onto/thesynapse_166_0.txt","reverseChoice":"neuron_onto/thesynapse_166_0.txt"},{"dataset":"bright-biology","qid":"65","query":"Why do human eyes glow red on a photo and dog's eyes green?\nPrevious Research\nI know that cameras sometimes come with an anti \"red eye\" functionality that involves two flashes, presumably the second flash coincides with the shutter opening.\nQuestion\nWhy do camera flashes sometimes lead to photos of humans with red eyes? And why do dog's eyes appear green?\nRelated\nThe reason for the green dog's eyes has been answered quite shortly under the question \"why-do-dogs-eyes-glow-green-on-a-photo\", but that question does not answer my specific question: Why do humans have red eyes on flashed photos and why are dog's eyes green?","confidence":0.87,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"dog_eyes_glow/Tapetum_lucidum_3.txt","reverseDid":"dog_eyes_green/Tapetum_lucidum_3.txt","forwardChoice":"dog_eyes_glow/Tapetum_lucidum_3.txt","reverseChoice":"dog_eyes_green/Tapetum_lucidum_3.txt"},{"dataset":"bright-biology","qid":"66","query":"How do baby animals that primarily subsist on cellulose get their initial gut flora?\nIn the case of mammals like giraffes and koalas, is that bacteria common on the plants they eat so when a baby starts to try to stick something besides its mother's milk in its mouth, it can't digest the cellulose at all the first time, but along with the cellulose, into its mouth went some cellulose-digesting flora that then begins a lifelong colony in the animal's gut? Is that about how it happens?","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"baby_animals_cellulose/Cecotrope_6.txt","reverseDid":"baby_animals_cellulose/Cecotrope_6.txt","forwardChoice":"baby_animals_cellulose/Cecotrope_6.txt","reverseChoice":"baby_animals_cellulose/Cecotrope_6.txt"},{"dataset":"bright-biology","qid":"69","query":"Why does the butterfly have a cocoon stage in its life cycle?\nWhy does the butterfly have a cocoon stage in its life cycle? It could have simply grown up within the egg/cocoon, and emerged as a butterfly. Instead it is first an egg, then a worm, then a chrysalis, and finally after some time a butterfly.","confidence":0.2,"flipped":true,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"butterflies_stage/butterflymomentcocoo_19_1.txt","reverseDid":"butterflies_stage/whatsthedifferencech_22_0.txt","forwardChoice":"butterflies_stage/butterflymomentcocoo_19_1.txt","reverseChoice":"butterflies_stage/whatsthedifferencech_22_0.txt"},{"dataset":"bright-biology","qid":"7","query":"Why does cracking a joint make noise?\nWhat causes the noise when you crack a joint? Is joint cracking harmful?","confidence":0.92,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"cracking_joint/Joint_cracking_0.txt","reverseDid":"cracking_joint/Joint_cracking_0.txt","forwardChoice":"cracking_joint/Joint_cracking_0.txt","reverseChoice":"cracking_joint/Joint_cracking_0.txt"},{"dataset":"bright-biology","qid":"70","query":"Are there lifeforms that have more than 2 sexes?\nAre there any lifeforms on earth that have more than male and female biological sexes?\nIf so, are all three mates required for reproduction or is there something else going on?\nIf not, why not? There are species that have one sex and there are species with 2 sexes. I get some of the basics of why there are two instead of one, but would those benefits not be even greater if there were three sexes?\nNOTE: I am of course only talking about sexual reproduction. I have no intention or desire to talk about transgender or LGBT issues nor am I wanting to make any points on the issue. This is solely a question about sexes in biology and not human social constructs.\nAlso, I am a computer programmer, not a biologist. Please forgive me for not knowing any of the terminology.","confidence":0.49,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"more_than_two_sexes/Mating_type_0.txt","reverseDid":"more_than_two_sexes/Mating_type_0.txt","forwardChoice":"more_than_two_sexes/Mating_type_0.txt","reverseChoice":"more_than_two_sexes/Mating_type_0.txt"},{"dataset":"bright-biology","qid":"72","query":"Does muscle get bigger by increase in size of individual cells or increase in number?\nSomewhere in the back of my mind, I have the claim that a muscle never increases its amount of cells but, if the muscle gets bigger, it's simply because individual cells get bigger.\nThe book Anatomy Trains on page 36 cites \"Changes in sarcomere length and physiological properties in immobilized muscle by Williams et al\" when it makes the claim :\nStretched, a muscle will attempt to recoil back to its resting length before giving up and adding more cells and sarcomeres to bridge the gap.\nIs that true? Do muscles increase the number of their cells in that way?","confidence":0.59,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"muscle_bigger/Muscle_hypertrophy_4.txt","reverseDid":"muscle_bigger/Muscle_hypertrophy_4.txt","forwardChoice":"muscle_bigger/Muscle_hypertrophy_4.txt","reverseChoice":"muscle_bigger/Muscle_hypertrophy_4.txt"},{"dataset":"bright-biology","qid":"74","query":"Why do birches have a white bark?\nSeveral species of the tree genres Betula have a white bark,especially when they are young. This is quite peculiar to trees - at least in central Europe; so I'm wondering if there is a ecological reason behind it.\nThe first idea I have is that they protection against the sun. But normally protection of nature against the sun is to make the opposite: put more pigments in the skin, so that the sun does not damage the underlying DNA. If this is the explanation, how do other tree species protect against this?","confidence":0.42,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"birches_white_bark/Sun_scald_4.txt","reverseDid":"birches_white_bark/Sun_scald_4.txt","forwardChoice":"none","reverseChoice":"birches_white_bark/Sun_scald_4.txt"},{"dataset":"bright-biology","qid":"78","query":"Is it possible for a human to wake up in a wrong way?\nThere's an old folk saying that goes like \"He got out of bed on a wrong foot\" - to indicate that the person's day is going poorly because of the way that person woke up.\nIs it is possible for a human to disrupt the awakening process, causing negative cognitive side effects for the rest of the day? If so, how long does it take to restore normal cognition?\nThere's this related question that I asked - if different parts of a human brain can be asleep independently of each other? This question is a variation on that one, and specifically deals with the process of awakening or activating brain regions in the morning.\nI'm aware of the concept of \"Sleep inertia\". However, it does not seem to be the phenomenon in question, as sleep inertia is frequently mentioned in the context of napping. (where napping over a certain time leads to sleep inertia). Additionally, sleep inertia has been described in terms of decline of motor activity, and not as cognitive side effects lasting for a whole day.\nFrom personal experience, it seems that I'm doing best if I get out of bed immediately following a natural awakening, even if it is rather early, as opposed to snoozing for another 30-40 minutes.","confidence":0.66,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"wake_up_wrong/Sleep_inertia_0.txt","reverseDid":"wake_up_wrong/Sleep_inertia_0.txt","forwardChoice":"wake_up_wrong/Sleep_inertia_0.txt","reverseChoice":"wake_up_wrong/Sleep_inertia_0.txt"},{"dataset":"bright-biology","qid":"8","query":"What is the longest-lasting protein in a human body?\nProtein life times are, on average, not particularly long, on a human life timescale. I was wondering, how old is the oldest protein in a human body? Just to clarify, I mean in terms of seconds/minutes/days passed from the moment that given protein was translated. I am not sure is the same thing as asking which human protein has the longest half-life, as I think there might be \"tricks\" the cell uses to elongate a given protein's half-life under specific conditions.\nI am pretty sure there are several ways in which a cell can preserve its proteins from degradation/denaturation if it wanted to but to what extent? I accept that a given protein post-translationally modified still is the same protein, even if cut, added to a complex, etc. etc.\nAnd also, as correlated questions: does the answer depend on the age of the given human (starting from birth and accepting as valid proteins translated during pregnancy or even donated by the mother)? What is the oldest protein in a baby's body and what is in a elderly's body? How does the oldest protein lifetime does in comparison with the oldest nucleic acid/cell/molecule/whatever in our body?","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"longest-lasting_protein/Elastin_4.txt","reverseDid":"longest-lasting_protein/Elastin_4.txt","forwardChoice":"longest-lasting_protein/Elastin_4.txt","reverseChoice":"longest-lasting_protein/Elastin_4.txt"},{"dataset":"bright-biology","qid":"83","query":"Why do adult insects have 6 legs?\nApparently, there is an advantage to having 6 legs in the insect world. What is that advantage, if anything? Why would such an advantage exist for insects, but not for other, larger land animals?\nWhat do those middle legs do that the front and hind legs cannot, if anything? Are they really essential, or do they just generate more muscle power?","confidence":0.86,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"insects_attracted_to_light/Insect_8_4.txt","reverseDid":"insects_attracted_to_light/Insect_8_4.txt","forwardChoice":"insects_attracted_to_light/Insect_8_4.txt","reverseChoice":"insects_attracted_to_light/Insect_8_4.txt"},{"dataset":"bright-biology","qid":"84","query":"Why is funnel web spider venom so lethal to humans and not so much for other mammals?\nAccording to the information from this article,\nPeople and other primates are exquisitely sensitive to funnel web venom but, intriguingly, other mammals such as mice, rabbits, guineapigs, dogs and cats are relatively immune and often survive 100 times the human lethal dosage.\nWhy is the funnel web spider venom so much more lethal for humans and primates than other mammals?","confidence":0.33,"flipped":true,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"spider_venom_lethal/pnas2004516117_1_2.txt","reverseDid":"spider_venom_lethal/pnas2004516117_2_3.txt","forwardChoice":"spider_venom_lethal/pnas2004516117_1_2.txt","reverseChoice":"spider_venom_lethal/pnas2004516117_2_3.txt"},{"dataset":"bright-biology","qid":"86","query":"Is sexual reproduction outside the same biological family possible? Has it ever occured successfully?\nAre there any examples of two species taxonomically classified in different biological families that have successfully hybridized and produced viable offspring? If not, is there an example of where reproduction occured with non-viable offspring?\nTo be clear, I mean regular sexual reproduction that could occur in the natural world outside a lab. Even so, I'd be curious to know if it could even occur in a lab without direct genetic manipulation.\nFor example, grolar bears which are ursid hybrids between different species in the Ursus genus are known to exist. Also ursid hybrids between bear species in different genuses have been produced in captivity (sloth bear Melursus ursinus x Malayan sun bear Ursus malayanus and sloth bear x Asiatic black bear Ursus thibetanus). Would an extra-familial hybridisation be possible? Would this be more likely in the plant kingdom?\nThis question is inspired by a separate question on the Gardening SE which hints at a general lack of understanding of the genetic similarity required for cross-pollination in plants. It made me wonder whether there are any exceptions to the general assumption that extra-familial hybridisation is impossible.","confidence":0.66,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"humans_closest_relatives_after_primates/Primate_3_14.txt","reverseDid":"humans_closest_relatives_after_primates/Primate_3_14.txt","forwardChoice":"humans_closest_relatives_after_primates/Primate_3_14.txt","reverseChoice":"humans_closest_relatives_after_primates/Primate_3_14.txt"},{"dataset":"bright-biology","qid":"87","query":"Below which temperature human muscles don't work?\nWhen one exposes theirs body parts to cold environment (especially foot and hands), they may be numb, with temporarily blocked both muscles.\nWhat is typical body temperature below which human muscles don't work properly?\nMoreover, what is the mechanism behind it?\nEDIT:\nInterested in case when muscles almost cease to work (e.g one is not able to grasp a cup), not just in small perturbations to strength.","confidence":0.4,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"below_temperature_muscles_not_work/documentrepidrep1typ_3_5.txt","reverseDid":"below_temperature_muscles_not_work/documentrepidrep1typ_3_5.txt","forwardChoice":"none","reverseChoice":"below_temperature_muscles_not_work/documentrepidrep1typ_3_5.txt"},{"dataset":"bright-biology","qid":"89","query":"By what mechanism can hypoglycemia induce fainting?\nHypoglycemia (Low blood sugar) is said to be among the potential causes of human-faintings. I was wondering by which mechanism this happens. Google search results are abundant; there are lists of various causes and solutions, but I couldn't find a direct answer to my question.\nIs it a controlled reduction of heart function to save energy? Is it an uncontrolled shock reaction (malfunction)? I'm looking for an answer along those lines.\nThanks.","confidence":0.7,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"fainting_mechanism/Reflex_syncope_2.txt","reverseDid":"fainting_mechanism/Reflex_syncope_2.txt","forwardChoice":"none","reverseChoice":"none"},{"dataset":"bright-biology","qid":"94","query":"Can a neuron make a synapse onto itself?\nI was wondering if a neuron can make a synapse to itself? I suspect that it would be extremely unusual for a neuron to do this. Anyway, has anyone seen even a single instance of this?\nIs the process by which a neuron 'knows' not to make a synapse onto itself is known?","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"neuron_onto/Autapse_0.txt","reverseDid":"neuron_onto/Autapse_0.txt","forwardChoice":"neuron_onto/Autapse_0.txt","reverseChoice":"neuron_onto/Autapse_0.txt"},{"dataset":"bright-biology","qid":"96","query":"How does the eye know whether to focus further out or nearer in order to bring a blurry object into focus?\nIf the eye sees an object, but the object is out of focus, the eye could be focusing too near or too far. How does the eye (or the brain) distinguish one situation from the other? How does it know whether to focus closer or further?\nI'm interested in the case when:\nonly one eye is open (There must be another way besides parallax clues).\nThe object is an unfamiliar size\nThe object is an unknown distance from the eye.\nIdeas so far\nDoes the eye try focusing either a little nearer or further and then if that doesn't help to make the object clearer then the eye focuses in the opposite direction?\nOr is there some light-sensitive apparatus that is always a focused a little nearer or further than the rest of the eye, and so for instance if the cells which are focused further out are giving a clearer picture than the other cells then the eye knows to focus further out?\nOr is the blurriness of a object closer than the focal point a distinguishably different sort of blurriness than an object beyond than the focal point?\nOr is there some other mechanism altogether?","confidence":0.7,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"eye_focus/Accommodation_(vertebrate_eye)_0.txt","reverseDid":"eye_focus/Accommodation_(vertebrate_eye)_0.txt","forwardChoice":"none","reverseChoice":"none"},{"dataset":"bright-biology","qid":"97","query":"Why can't our eyes smoothly transition from side to side without focusing on a moving object?\nWhy are we not able to slowly and smoothly look from side to side, or up and down in a single and smooth transition, given that we are able to do this if our eyes are focused on a moving object?","confidence":0.63,"flipped":true,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"eyes_smooth_transition/Smooth_pursuit_0.txt","reverseDid":"eyes_smooth_transition/Smooth_pursuit_5.txt","forwardChoice":"eyes_smooth_transition/Smooth_pursuit_0.txt","reverseChoice":"eyes_smooth_transition/Smooth_pursuit_5.txt"},{"dataset":"bright-biology","qid":"99","query":"What creates the feeling of 'excess' blood pressure to an area of the body?\nIf I hang upside down, and feel blood rushing to my head, what structures are actually responsible for me \"feeling\" this excess flow of blood? Baroreceptors? Mechanoreceptors? Something else?\nRegardless, how does this process actually work? How do I \"feel\" excess blood pressure to a certain area of my body?","confidence":0.68,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"feel_excess_pressure/Baroreceptor_0.txt","reverseDid":"feel_excess_pressure/Baroreceptor_0.txt","forwardChoice":"feel_excess_pressure/Baroreceptor_0.txt","reverseChoice":"feel_excess_pressure/Baroreceptor_0.txt"},{"dataset":"bright-economics","qid":"13","query":"Log Transformation of Zeros (Slave Trade Data)\n\nI am trying to log transform data on slave trade voyages from the Database by David Eltis,The Trans-Atlantic Slave Trade Database.\n\nThe Problem I am facing: Many values are 0. My first idea was to assign value 1 to all values which are 0, so after log transforming they are 0. However, since I am taking average voyages per year, for many years the value is < 1, leading to negative values after the log transformation.\n\nSo now I have smaller values for years in which there was slave trade (but <1) compared to years when there was no slave trade.\n\nHow does one adress such a problem?","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"hyperbolicsine/BellemareWichmanIHSFebruary2019pdf_0.txt","reverseDid":"hyperbolicsine/BellemareWichmanIHSFebruary2019pdf_0.txt","forwardChoice":"hyperbolicsine/BellemareWichmanIHSFebruary2019pdf_0.txt","reverseChoice":"hyperbolicsine/BellemareWichmanIHSFebruary2019pdf_0.txt"},{"dataset":"bright-economics","qid":"17","query":"Proving existence of a unique pure strategy Nash Equilibrium in two-person continuous games\n\nI am working on a game theory exercise and I want to prove existence and uniqueness of pure strategy NE for a 2 person game with continuous strategy spaces. Lets call the payoff functions 𝜋1\n and 𝜋2\n for players 1 and 2. The strategy spaces are simple one dimensional intervals in Euclidean space that are common across players (𝑠1,𝑠2∈𝑆=[𝑎,𝑏]\n). What do I need to do to show that the payoff functions are diagonally strictly concave? I am having a hard time wrapping my head around Rosen's paper and I don't need many dimensional strategy spaces and n players for now. I was wondering if there was a simpler, easy to understand version of the result for my simplified game. If someone could also provide some intuition for that condition, it would also be a massive help! Thanks","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"nash_continuous/48cb7ae4210825b7de3d7fc0bcc8553fMIT6254S10lec06bpdf_7.txt","reverseDid":"nash_continuous/48cb7ae4210825b7de3d7fc0bcc8553fMIT6254S10lec06bpdf_7.txt","forwardChoice":"nash_continuous/48cb7ae4210825b7de3d7fc0bcc8553fMIT6254S10lec06bpdf_7.txt","reverseChoice":"nash_continuous/48cb7ae4210825b7de3d7fc0bcc8553fMIT6254S10lec06bpdf_7.txt"},{"dataset":"bright-economics","qid":"18","query":"Quadratic Form Single Summation notation\n\nI'm trying to translate what feels like a double summation notation , compacted into a single summation. It's the summation definition of quadratic forms.\n\n𝑄(𝑥1,𝑥2.....𝑥𝑛)=∑𝑖≤𝑗𝑎𝑖𝑗𝑥𝑖𝑥𝑗\n\nI assume quadratic forms can be written as the less compact double summation?\n\n𝑄(𝑥1,𝑥2.....𝑥𝑛)=∑𝑛𝑖=1∑𝑛𝑗=1𝑎𝑖𝑗𝑥𝑖𝑥𝑗\n\nTo count this we would:\n\nFix the outer summation first, so in this case we would be fixing rows and going across columns. Alternately we could switch the summation so we fixed columns and counted rows. This holds for any 𝑛•𝑚\n matrix i believe?\nQuestion: I suppose the 𝑖≤𝑗\n notation has to correspond to this. But it's not immediately clicking?","confidence":0.39,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"quadratic_form/summationpdf_3.txt","reverseDid":"quadratic_form/summationpdf_3.txt","forwardChoice":"quadratic_form/summationpdf_3.txt","reverseChoice":"quadratic_form/summationpdf_3.txt"},{"dataset":"bright-economics","qid":"19","query":"Derivative to ln(K(t)) in the RBC model\n\nIn the calculation of the equation of motion for capital in the RBC model, I came across this equation:\n\nd ln K_(t+1) / d ln K_t = (d K_(t+1) / d K_t) * (K_t / K_(t+1))\n\nCan someone explain what are the mathematical steps in between? I don't see how exactly the derivative to ln(K(t)) gets us an almost elasticity-like equation.\n\nWould be thankful for any leads. :)","confidence":0.92,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"elastic_substitution/notesRBCpdf_7.txt","reverseDid":"elastic_substitution/notesRBCpdf_7.txt","forwardChoice":"elastic_substitution/notesRBCpdf_7.txt","reverseChoice":"elastic_substitution/notesRBCpdf_7.txt"},{"dataset":"bright-economics","qid":"20","query":"Why have bank deposits gone down with rising interest rates?\n\nFrom an opinion piece in the Washington Post:\n\n\"Since the Federal Reserve began to raise rates about a year ago, deposits leaving the banking sector have totaled nearly $1 trillion\".\n\nWhy would bank deposits go down while interest rates are going up? I thought the higher interest rates would attract more deposits.","confidence":0.32,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"deregulation/48614398pdf_17.txt","reverseDid":"deregulation/48614398pdf_17.txt","forwardChoice":"none","reverseChoice":"deregulation/48614398pdf_17.txt"},{"dataset":"bright-economics","qid":"22","query":"Why do all filled bids receive the same rate, which is the rate of the lowest filled bid, during the US Treasury auction process?\n\nThis question pertains to the US Treasury auction process. {1} states:\n\nTreasury auctions are designed to minimize the cost of financing the national debt by promoting broad, competitive bidding and liquid secondary market trading.\n\nHowever, from my understanding, all competitive and noncompetitive bids that are filled receive the same rate, which is the rate of the lowest filled bid.. This seems to contradict {1}, seems it does not minimize the cost of financing the national debt.\n\nWhy do all competitive and noncompetitive bids that are filled receive the same rate, which is the rate of the highest filled bid, during the US Treasury auction process?","confidence":0.6,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"bid_auction/ci112pdf_3.txt","reverseDid":"bid_auction/ci112pdf_3.txt","forwardChoice":"bid_auction/ci112pdf_3.txt","reverseChoice":"bid_auction/ci112pdf_3.txt"},{"dataset":"bright-economics","qid":"25","query":"A question on Marx' \"Value, price and profit\"\n\nIn his lecture Value, price and profit Karl Marx argues that profit is made by capitalists by (i) selling commodities for their real price (= real value expressed in money), (ii) paying the workers (down the complete supply chain) as wages the real value (expressed in money) of the commodities they produce but (iii) letting them work more time than needed for the production of these commodities (= produce more commodities, but unpaid for).\n\nI don't understand why he insists on (ii) and (iii) instead of just saying that the capitalists pay the workers simply less than the real price (by which they sell the commodities).\n\nWhat's the difference between \"working full time for half wages\" and \"working half time for full wages and half time for no wages\"?","confidence":0.46,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"valuepriceprofit/ch02htmc10_38.txt","reverseDid":"valuepriceprofit/ch02htmc10_38.txt","forwardChoice":"valuepriceprofit/ch02htmc10_38.txt","reverseChoice":"valuepriceprofit/ch02htmc10_38.txt"},{"dataset":"bright-economics","qid":"26","query":"Do inheritances break Piketty's r>g model's conclusions?\n\nIn Capital in the Twenty-First Century Piketty suggests that if the return on capital is higher than the economic growth, then wealth inequality in society will rise.\n\nYet, this model seems to ignore that people leave their inheritance to more than one person on average and form marriges with people of not ideally equal wealth. I.e. if a billionare marries a millionare and have three children to whom they pass their wealth after 50 years, they'll pass $1000𝑚+$1𝑚3(1+𝑟)50\n to each of them, making the society more equal if r is small enough.\n\nDoes such effect of inheritances break the model's conclusions (and inequality may decrease when r is significantly higher than g) or the effect is negligible?","confidence":0.45,"flipped":true,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"inheritance_inequality/inheritanceandwealthinequalityevidencefrompopulationregisters_17.txt","reverseDid":"inheritance_inequality/howdoinheritancesshapewealthinequalitytheoryandevidence_30.txt","forwardChoice":"inheritance_inequality/inheritanceandwealthinequalityevidencefrompopulationregisters_17.txt","reverseChoice":"inheritance_inequality/howdoinheritancesshapewealthinequalitytheoryandevidence_30.txt"},{"dataset":"bright-economics","qid":"27","query":"What will be the Effect of the Russian oil Price Cap\n\nG7 countries have just announced that they will not buy oil from Russia unless the price is USD 60/barrel or below. What will be the effect of this cap- will it be an effective economic sanction? Why can’t Russia sell the oil to a non G7 intermediary who sells it to G7? And what will it do to the market price of oil ?","confidence":0.48,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"oil_price_cap/businessreview20220928willanoilpricecapmanagetoreducethepdf_0.txt","reverseDid":"oil_price_cap/businessreview20220928willanoilpricecapmanagetoreducethepdf_2.txt","forwardChoice":"oil_price_cap/businessreview20220928willanoilpricecapmanagetoreducethepdf_0.txt","reverseChoice":"oil_price_cap/businessreview20220928willanoilpricecapmanagetoreducethepdf_2.txt"},{"dataset":"bright-economics","qid":"28","query":"Effects of retirement and unemployment on PPF\n\nWhich of the following will not shift a country's production possibility frontier (PPF) ? An increase in the age at which people retire or a fall in unemployment ?\n\nTo me, it is the increase in the age at which people retire that will not shift the country's PPF, this situation will represent a point in the bounded area (by the PPF, the axis and the origin), meaning that we can produce more but the PPF is not shifted, we still have the same technology and tools to produce. If a country can produce 100 cars and 50 tons of wheat, then if the people retire later, I do not see why the PPF could shift.\n\nIn the other hand, a fall in unemployment can imply more workers in development sectors of new technologies so it will shift it, we can produce more.\n\nThe correct answer was apparently fall in unemployment. This is very strange to me, according to me justification. I believe that if we make the assumptions that workers can only be employed in PPF sectors, then yes, a fall in unemployment will not shift the PPF. But that means that there are no people working in labs, in science, in developing new technologies etc. There are only people producing wheat and cars... Also, the question is not indicating by how much is the increase of age, so we can be very annoying, saying if I increase the age of retirement by 1 year or 1 day, it surely won't shift the PPF...\n\nAlso, mathematically speaking if fall in unemployment does not shift the PPF, then a rise in unemployment does not shift the PPF neither. And an increase at the age at which people retire ⟹\n a rise in unemployment ⟹\n no shift of PPF. So for me an increase in the age at which people retire will not shift a country's PPF.\n\nWho is right there ?","confidence":0.45,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"ppf_retire/ProductionE28093possibilityfrontier_6.txt","reverseDid":"ppf_retire/ProductionE28093possibilityfrontier_6.txt","forwardChoice":"ppf_retire/ProductionE28093possibilityfrontier_6.txt","reverseChoice":"ppf_retire/ProductionE28093possibilityfrontier_6.txt"},{"dataset":"bright-economics","qid":"29","query":"At what point would negative accounting equity for a central bank have macroprudential implications?\n\nThe yardstick for central bank \"health\" and/or \"credibility\" can be nebulous at times. It is sometimes argued that a balance-sheet assessment only does not capture the nuance of a central bank because the central bank's true \"asset\" is its ability to print money. But by the same token, central banks are now becoming bigger buyers of certain securities. In the case of Japan, the BOJ has over 50% of some maturities. The point being that when a central bank is expected to play a meaningful role when crises emerge, then it's \"liabilities\" will also be incalculable.\n\nFor commercial banks, equity serves as a quasi-clawback provision as it allows creditors to get repaid should the bank encounter extreme solvency issues. But for central banks, I'm not sure what \"equity\" really signals to anybody.\n\nQuestion\nAt what point does negative accounting equity register on the broader macroprudential radar and why? I suppose that even though one cannot have a \"run\" on a central bank, it could be seen as a BOP or FX crisis.","confidence":0.46,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"negative_commercial/bispap71pdf_2.txt","reverseDid":"negative_commercial/bispap71pdf_2.txt","forwardChoice":"negative_commercial/bispap71pdf_2.txt","reverseChoice":"negative_commercial/bispap71pdf_2.txt"},{"dataset":"bright-economics","qid":"32","query":"What are the consequences of a single global currency on trade?\n\nSorry for the general nature of the question but I find it difficult to wrap my head around the idea of a global currency.\n\nLet's say there existed a single global currency which was not controlled by a centralized entity. All countries use this currency as their national currency. Let's also assume there exists some mechanism by which countries can come to a consensus about changes to the global monetary system (money supply, interest rates, etc).\n\nMy question is, is there a way to achieve price stability in such a system? Would trade get in the way of the ability to achieve price stability, since the price of goods/services is different in each country?\n\nExcuse me if I am getting major concepts wrong. I am new to economics but extremely interested in this problem.","confidence":0.57,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"single_currency/Optimumcurrencyarea_5.txt","reverseDid":"single_currency/Optimumcurrencyarea_5.txt","forwardChoice":"single_currency/Optimumcurrencyarea_5.txt","reverseChoice":"single_currency/Optimumcurrencyarea_5.txt"},{"dataset":"bright-economics","qid":"33","query":"Who insures the FDIC in case it fails?\n\nI understand that the Federal Deposit Insurance Corporation (FDIC) failing is unlikely, but the probability of such a failure is still positive. In case the FDIC fails, who covers the customers' losses? Will the Federal Reserve surely step in to print money to cover FDIC's ass?","confidence":0.46,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"fdic/indexhtml_101.txt","reverseDid":"fdic/indexhtml_101.txt","forwardChoice":"fdic/indexhtml_101.txt","reverseChoice":"none"},{"dataset":"bright-economics","qid":"38","query":"How are stock prices determined in the following cases?\n\nI looked at this question already. I know there is an order book with bid and ask and that the price is updated when a match occurs. But I have two questions:\n\nWhat happens when the bid is higher than the ask? For example someone is ready to pay $101 per share for 100 shares and someone wants to sell 100 shares at $100. What will be the new price?\n\nWhat if there are multiple matches at an instance? Let's say we have someone wanting to buy 100 shares at $100 and someone wanting to sell 100 shares at $100. We also have someone wanting to buy 500 shares at $110 and someone wanting to sell 500 shares $110. What is the new price?","confidence":0.42,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"matching_price/matchingordersasp_5.txt","reverseDid":"matching_price/matchingordersasp_5.txt","forwardChoice":"none","reverseChoice":"matching_price/matchingordersasp_5.txt"},{"dataset":"bright-economics","qid":"39","query":"What's wrong with this argument that the fed's OMOs don't change the money supply\n\nIs there anything facially stupid about the following argument that fed open market operations don't affect the money supply? And if not, are there any professional economists who have made this argument?\n\nWhen the fed buys or sells treasury bonds they are not changing the broader stock of highly liquid assets that include treasury bonds (is that m3?), since they are just exchanging one of the components (cash) for another (govt bonds). Since there are no reserve requirements for commercial banks -- only capital requirements -- this doesn't actually affect the rate of credit creation since a bank that exchanges a dollar of central bank reserves for a dollar of us treasury debt is just as well capitalized as it was before.","confidence":0.41,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"omo_money_supply/rqt1212gpdf_11.txt","reverseDid":"deposit_interest/moneycreationinthemoderneconomypdf_5.txt","forwardChoice":"omo_money_supply/rqt1212gpdf_11.txt","reverseChoice":"deposit_interest/moneycreationinthemoderneconomypdf_5.txt"},{"dataset":"bright-economics","qid":"46","query":"What is the economic nature of water?\n\nHow should one classify water in economic terms?\n\nIs it a commodity, a natural resource, can it be both? Does it depend on how it is being used (e.g., as input/raw material in some process)? I was curious about all the ways water could be characterized economically (e.g., as a rival good, a commodity etc.) and would be grateful for some pointers.","confidence":0.4,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"water_economic_nature/1627_0.txt","reverseDid":"water_economic_nature/nature_175.txt","forwardChoice":"water_economic_nature/1627_0.txt","reverseChoice":"water_economic_nature/nature_175.txt"},{"dataset":"bright-economics","qid":"47","query":"Does low nominal interest rate encourage lending?\n\nIn expansionary monetary policy, it's written:\n\nThe Fed purchases more government bonds to drive down interest rates and increase the money supply.\n\nNow, low interest rate can infer two things:\n\nPeople find it easier to take loans and invest. So, investment goes up.\n\nBut, at the same time, low interest rate may mean that lenders may not be very keen on giving out loans. For example, people would find no use in keeping cash into bank accounts which provide low interest rates. This may drive down lending, and hence investment.\n\nIs point (2) correct? If so, why is low n.i.r. considered good for economic growth?\n\nThanks","confidence":0.15,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"nominal_interest_rate/ECTA17408_20.txt","reverseDid":"inflation_interest/howdoesraisinginterestrateshelpinflation_37.txt","forwardChoice":"nominal_interest_rate/ECTA17408_20.txt","reverseChoice":"inflation_interest/howdoesraisinginterestrateshelpinflation_37.txt"},{"dataset":"bright-economics","qid":"49","query":"Optimal stopping (reference request)\n\nI am interested in the following optimal stopping problem:\n\nOn each day, a number 𝑎𝑖\n is drawn from a (possibly fixed) distribution.\nI can either stop now, getting a payoff of 𝑎𝑖\n, or wait for a later draw.\nIn principle, this could go on forever. However, future payoffs are discounted at a (possibly constant) rate.\nI know this kind of problem has been analysed extensively. Can anyone recommend some references on how one characterises optimal strategies in this context?","confidence":0.35,"flipped":true,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"optimal_stopping/2351065_30.txt","reverseDid":"optimal_stopping/2351065_28.txt","forwardChoice":"none","reverseChoice":"optimal_stopping/2351065_28.txt"},{"dataset":"bright-economics","qid":"52","query":"Is there a long-run tradeoff between inflation and unemployment?\n\nIn most first-year undergrad macroeconomics courses, students are taught about the Phillips curve and how whilst there may exist a tradeoff between inflation and unemployment in the short-run, there is no such thing in the long-run as a result of agents adjusting inflation expectations. Is this result valid or could it be the case that the natural rate of unemployment changes with the output gap, in which case there could exist a long-run trade-off between inflation and unemployment?","confidence":0.21,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"inflation_unemployment/S0304393215000793_12.txt","reverseDid":"inflation_unemployment/S0304393215000793_12.txt","forwardChoice":"inflation_unemployment/S0304393215000793_12.txt","reverseChoice":"inflation_unemployment/S0304393215000793_12.txt"},{"dataset":"bright-economics","qid":"56","query":"GDP: business incorporation location vs citizenship criteria [duplicate]\n\nThis question already has answers here:\nTextbooks claim that the difference between GDP and GNP (or GNI) is about geography vs citizenship--is this correct? (2 answers)\nClosed 2 years ago.\nSuppose a US citizen resides in Germany. He is a business-owner in Germany and the business is legally incorporated in Germany. However, he is a US citizen. The value of the good produced by this business will be part of Germany's GDP since they are produced in Germany. But do they count towards German GDP since he is a resident and the business is incorporated there, or towards US GDP since he is a US citizen?","confidence":0.63,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"fill_in_data_gap/grossdomesticproductGDP_40.txt","reverseDid":"us_german/chapter02pdf_5.txt","forwardChoice":"fill_in_data_gap/grossdomesticproductGDP_40.txt","reverseChoice":"us_german/chapter02pdf_5.txt"},{"dataset":"bright-economics","qid":"57","query":"Difference between two different ways of adjusting for inflation daily given annual rate\n\nThis is a follow up question to this\n\nI found two different ways to \"adjust for inflation\" on a daily basis given an annual rate (without doing daily compounding). Both yield the same result at the end of the timeframe however I can't determine if there is any error in the interpolated result.\n\n𝑟:annual rate\n𝑡:time in days\n𝑦𝑒𝑎𝑟𝑠:total time in years, constant\n𝑃𝑉:Present Value, constant\n𝐹𝑉(𝑡):Future Value for a given time 𝑡 (in days)\n\nFor example, if 𝑦𝑒𝑎𝑟𝑠=2\n then 𝑡∈[0,730]\n\nPlots below are generated using 𝑟=0.50\n and 𝑃𝑉=1000\n\nThe first approach is using Giskard's proposal (see related anwer) and let the rate fixed and let the time vary:\n𝑥=1+𝑟‾‾‾‾‾√365−1\n𝐹𝑉(𝑡)=𝑃𝑉(1+𝑥)𝑡\n\nThe second solution fixes the time and changes the interest rate based on time:\n𝑥(𝑡)=𝑟365∗𝑡365\n𝐹𝑉(𝑡)=𝑃𝑉(1+𝑥(𝑡))𝑦𝑒𝑎𝑟𝑠\n\nI think the second approach is incorrect because when computing 2 years, the value for one year changed from 666.67\n to ≈640\n whereas in the first method the original value is kept but I cannot justify why this happens.","confidence":0.58,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"adjustinflation/Compoundinterest_20.txt","reverseDid":"adjustinflation/Compoundinterest_20.txt","forwardChoice":"adjustinflation/Compoundinterest_20.txt","reverseChoice":"adjustinflation/Compoundinterest_20.txt"},{"dataset":"bright-economics","qid":"59","query":"Can there be a game where there are no opponents?\n\nI am considering a scenerio where all the players a in collaboration with each other in an attempt to maximize some profit. However, each player is not 100% sure of the strategy of other player so have to figure out the strategy of other player to play his own strategy to collectively maximize some profit.\n\nIs this covered by game theory? i.e. there are no opposition or state of nature?","confidence":0.63,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"coordination_game/Coordinationgame_5.txt","reverseDid":"coordination_game/Coordinationgame_5.txt","forwardChoice":"coordination_game/Coordinationgame_5.txt","reverseChoice":"coordination_game/Coordinationgame_5.txt"},{"dataset":"bright-economics","qid":"65","query":"New Business - How do I set a price for my product?\n\nThis is my first question here, but since I am used to the Stack Exchange format I will give as much detail about my problem without giving an overload of details.\n\nIn a nutshell I work with US weather radar data and decided I could make an app for storm chasers and emergency management. I have done research and know about what my fees to customers will be, and I have an expectation of how many signups I will have. During the process of creating this, I have also created some custom data that I realized I could sell to weather data provider companies. They will not compete with my web app, so this would be great extra income.\n\nThis is where the problem is. A company has expressed interest in licensing this data from me on a monthly basis. Besides the cost of my servers ($1,000 to $2,000 a month?) I do not know how much to offer this data for. The last thing I want to ask them is \"how much would you like to pay?\".\n\nTo them, this would be helpful as they can re-sell the data to their own emergency management customers. I feel like it would be worth about $1,000 to $3,000 a month on top of server costs. It's not super revolutionary data, but it's going to be useful for them and they might be expecting to pay $8,000 a month (random guess). I am afraid to ask for that right off the bat because they may laugh it off. I am not a bashful person, but I don't want to come across so far left field that they just say nevermind to the whole thing.\n\nI am looking for any sort of advice on how to approach this problem. I think the main problem with something new like this is that the actual value of the product seems either somewhat arbitrary, or the actual value could be anything within a range from $3,000 to $8,000 or more.","confidence":0.43,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"blotto/itemid31283026_270.txt","reverseDid":"weather_data/businessmodelcanvastemplate_15.txt","forwardChoice":"blotto/itemid31283026_270.txt","reverseChoice":"none"},{"dataset":"bright-economics","qid":"70","query":"Irreversibility in the Creation of Value\n\nIn his book The Origin of Wealth, Eric D. Beinhocker says that\n\nA pattern of matter, energy, and/or information has economic value if the following three conditions are jointly met:\n\nIrreversibility: All value-creating economic transformations and transactions are thermodynamically irreversible.\nEntropy: All value-creating economic transformations and transactions reduce entropy locally within the economic system, while increasing entropy globally.\nFitness: All value-creating economic transformations and transactions produce artifacts and/or actions that are fit for human purposes.\nThese conditions seems pretty logical for me, particularly the \"fitness\" thing. However, I wonder about the \"irreversibility\" part: it sounds logical to state that, say, once a tree is transformed into a chair it cannot be turned into a tree again; so, making a chair is clearly irreversible. In the same vein, if a barber cut the hair of someone, he or she can not \"uncut\" it - irreversibility, of course.\n\nBut what if I was a doorman? My job would be to open a door for the others the whole day, which is a service that many people value to some extent, i.e., I would be creating some value in the economic sense of the term, right? But I can close the door as easily as I can open it, so it is something clearly reversible. Thus, it seems obvious I may do something \"reversible\" that is valued by someone - should I conclude Beinhocker is wrong or - I as suspect - I am missing something?","confidence":0.53,"flipped":true,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"Irreversibility/valuecreation_23.txt","reverseDid":"Irreversibility/valuecreation_21.txt","forwardChoice":"Irreversibility/valuecreation_23.txt","reverseChoice":"Irreversibility/valuecreation_21.txt"},{"dataset":"bright-economics","qid":"74","query":"IV regression: first stage in logs, second stage in levels?\n\nI have a regression in levels, derived from theory.\n\nI want to instrument one of the variables, but the best instrument I find has a weak correlation to the endogenous variable in levels, and a strong correlation in logs. Both are very heteroskedastic.\n\nIs it possible to somehow instrument with the first stage in logs, and the second stage in levels?","confidence":0.27,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"frisch/Instrumentalvariable_126.txt","reverseDid":"forbidden_regression/notesonforbiddenregressionspdf_4.txt","forwardChoice":"none","reverseChoice":"none"},{"dataset":"bright-economics","qid":"78","query":"Why isn't economic growth defined in terms of the increase in national wealth?\n\nEconomic growth is defined as the increase in production or output per unit time. Why isn't it instead defined in terms of the increase in national wealth?","confidence":0.29,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"economy_wealth/wealthasp_2.txt","reverseDid":"capitalism_central_bank/adamsmithandcapitali_81.txt","forwardChoice":"economy_wealth/wealthasp_2.txt","reverseChoice":"none"},{"dataset":"bright-economics","qid":"79","query":"Are there any behavioral macro models with rigorous micro-foundations?\n\nI am looking for some paper that tries to establish rigorous micro-foundations the behavioral New Keynesian (or any other) macro models.\n\nThis is surprisingly hard, most work on this topic (like De Grauwe 2012), simply starts already by setting up IS and LM curves and adding some sort of mix of rational and behavioral agents (mostly agents with non-rational expectations) on top of it. However, I can't find any paper that would provide full rigorous micro-foundations for such model (i.e. deriving IS and LM from the micro behavior of these rational and behavioral agents across time).\n\nThis is of course difficult, as without the rational expectations it is difficult to solve macro models, but on other hand it is hard to believe this issue would be ignored by the practitioners.\n\nConsequently, my question is: are there any papers that establish rigorous micro-foundations for behavioral macro models?","confidence":0.31,"flipped":true,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"micro_foundation/behavioralnewkeynesianmodelpdf_63.txt","reverseDid":"micro_foundation/behavioralnewkeynesianmodelpdf_0.txt","forwardChoice":"micro_foundation/behavioralnewkeynesianmodelpdf_63.txt","reverseChoice":"micro_foundation/behavioralnewkeynesianmodelpdf_0.txt"},{"dataset":"bright-economics","qid":"8","query":"Would a GDP measure be improved by excluding foreign interest paid?\n\nThe income method of calculating GDP is as follows: GDP = wages + profits + rents + interest + depreciation + taxes + NFFI. If an economy has high external debt, for instance, because it used external financing to buy machinery and equipment, then foreign interest payments will be high. In that case, wouldn't GDP (per capita) be a poor measure of economic well-being since a significant portion of the generated income is leaving the home economy?\n\nWouldn't excluding foreign interest payments in calculating GDP/ income give us a better measure since this represents what income is available to the home economy?","confidence":0.4,"flipped":true,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"gdp_wellbeing/howdoyoucalculategdpincomeapproachasp_5.txt","reverseDid":"gdp_wellbeing/howdoyoucalculategdpincomeapproachasp_4.txt","forwardChoice":"gdp_wellbeing/howdoyoucalculategdpincomeapproachasp_5.txt","reverseChoice":"gdp_wellbeing/howdoyoucalculategdpincomeapproachasp_4.txt"},{"dataset":"bright-economics","qid":"81","query":"Economic growth in a DSGE model, despite mean-zero shocks\n\nThe DSGEs I've seen have steady-states, and mean-zero shocks.\n\nCan these predict growth in GDP / capital etc?\n\nIs this possible despite them being equilibrium models, or do you have to completely change your approach and switch to a Solow-Swan-type model to predict GDP growth?","confidence":0.33,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"economic_growth/Dynamicstochasticgeneralequilibrium_76.txt","reverseDid":"economic_growth/Dynamicstochasticgeneralequilibrium_76.txt","forwardChoice":"economic_growth/Dynamicstochasticgeneralequilibrium_76.txt","reverseChoice":"economic_growth/Dynamicstochasticgeneralequilibrium_76.txt"},{"dataset":"bright-economics","qid":"83","query":"Large National Debt by borrowing money from foreign sources\n\nI have a question about the greater severity of large national debt caused by borrowing from foreign sources.\n\nI am reading a book about elementary Economics and am currently on a chapter discussing the strengths and weaknesses of demand-side policies.\n\nThe book (A-level Economics, Anderton, 2015, pp. 204) states the following:\n\nKeynesian economics states that so long as a government can print money to finance its deficit without fueling inflation or borrow money from the financial markets, then the National Debt is not a problem for the short term. Nearly all economists, however, would argue that, in the long term, large National Debts can be a problem particularly if they are financed mainly by borrowing money from foreigners.\n\nHowever, I am struggling to understand how they can be worse than borrowing from domestic debt markets (if I have stated the correct market i.e. from a domestic lender).\n\nMy understanding was that if the debt was financed through printing more money, and had to pay off the loans to a foreign lender, it would find it harder to reduce the supply of money via reversing quantitative easing i.e. selling bonds back into the bond market, so impending inflation in the long term would occur.\n\nHowever, the book does not qualify its statement regading large National Debt as above so I am not sure what the justification is for Anderton's statement above.\n\nWhy would large National Debts financed by foreign lenders be a greater problem than domestic lenders if the problem was to be solved by printing more money?","confidence":0.43,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"domestic_foreign/foreigndebtasp_6.txt","reverseDid":"domestic_foreign/foreigndebtasp_6.txt","forwardChoice":"domestic_foreign/foreigndebtasp_6.txt","reverseChoice":"domestic_foreign/foreigndebtasp_6.txt"},{"dataset":"bright-economics","qid":"84","query":"Krugman article - Government debt helps avoid a destructive scramble for cash?\n\nI was just reading this Krugman article which contains the words...\n\nI’ve already mentioned that having at least some government debt outstanding helps the economy function better. How so? The answer, according to MIT’s Ricardo Caballero and others, is that the debt of stable, reliable governments provides “safe assets” that help investors manage risks, make transactions easier and avoid a destructive scramble for cash.\n\nNow the \"help investors manage risks\" part I can understand, but \"make transactions easier\" and \"avoid a destructive scramble for cash\" are a mystery to me.","confidence":0.77,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"government_debt/whydebtgood_10.txt","reverseDid":"government_debt/whydebtgood_10.txt","forwardChoice":"government_debt/whydebtgood_10.txt","reverseChoice":"government_debt/whydebtgood_10.txt"},{"dataset":"bright-economics","qid":"85","query":"Is \"avoiding being the bearer of bad news\" an example of the \"principal-agent problem\"?\n\nFor example:\n\nA king designs a bridge, and he unknowingly does a bad job. He asks an engineer for her opinion, but the engineer fears that if she tells the truth, the dictator will be angry with her.\n\nMore generally, I'm thinking of situations in which the principal requests feedback/information from the agent, but fear of retribution prevents the latter from being honest.\n\nIt seems like a reasonable example of the principal-agent problem, with conflicting incentives, but I wasn't sure if this situation had its own name.","confidence":0.4,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"principal_agent/principalagentproble_1.txt","reverseDid":"volunteerdillema/Moralhazard_140.txt","forwardChoice":"none","reverseChoice":"none"},{"dataset":"bright-economics","qid":"88","query":"Non CES Production Functions\nI know CES production functions dominate economics, but I was curious, why? I've never seen a research paper or presentation utilize any form of a production function that is not CES.\n\nwhy is imposing CES so important in our models? ","confidence":0.37,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"ces_production/lecture4ces_7.txt","reverseDid":"ces_production/lecture4ces_7.txt","forwardChoice":"ces_production/lecture4ces_7.txt","reverseChoice":"ces_production/lecture4ces_7.txt"},{"dataset":"bright-economics","qid":"89","query":"What is the \"moderator for the relationship\" in interactive regression model?\n\nI have an interactive regresison model as below:\n\n𝑌𝑖,𝑡=𝛽0+𝛽1\n𝐷𝑖\n + 𝛽2\n𝑃𝑖,𝑡\n + 𝛽3\n(𝐷𝑖\n𝑃𝑖,𝑡\n) + other covariates + error terms\n\nIn this model, why 𝐷𝑖\n is called as the moderator for the relationship between 𝑃𝑖,𝑡\n and 𝑌𝑖,𝑡","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"relationship_moderator/moderatingvariable_1.txt","reverseDid":"relationship_moderator/moderatingvariable_1.txt","forwardChoice":"relationship_moderator/moderatingvariable_1.txt","reverseChoice":"relationship_moderator/moderatingvariable_1.txt"},{"dataset":"bright-economics","qid":"92","query":"What is social science and discussion about Type II error preference?\n\nToday, my senior lecturer in economics and finance class teaching about laws impact companies' operation told us one sentence \"Also keep in mind that in (at least social) science, we care (try to avoid) about Type II (making a claim on some new finding while it is actually not true), more than Type I error (rejecting some new findings while it is actually true). \"\n\nFrom this link, the definition of social science is: \"Social science is, in its broadest sense, the study of society and the manner in which people behave and influence the world around us.\"\n\nBased on this definition, why economics relating to social science, and what is other than social science","confidence":0.43,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"econ_social/typeiandtypeiierrors_21.txt","reverseDid":"econ_social/typeiandtypeiierrors_21.txt","forwardChoice":"econ_social/typeiandtypeiierrors_21.txt","reverseChoice":"econ_social/typeiandtypeiierrors_21.txt"},{"dataset":"bright-economics","qid":"93","query":"What is the difference between Impression Management and Signaling Theory?\n\nI'm interested in theories on how organisations shape their stakeholders' (especially consumers' and investors') perceptions and decisions. I read about Impression Management and Signaling Theory. They seam to be quite similar and I'm unsure when to apply which of them and where they differ. I could not find an article comparing both theories. Signaling Theory is especially for cases with information asymmetry but it seams like Impression Management is also more common in cases with scare information on one side.","confidence":0.32,"flipped":true,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"impression_management/Signallingeconomics_7.txt","reverseDid":"impression_management/Impressionmanagement_4.txt","forwardChoice":"none","reverseChoice":"none"},{"dataset":"bright-economics","qid":"95","query":"Bootstrap always valid under asymptotic Normality?\n\nIf an estimator is known to have an asymptotically normal distribution, is that sufficient for the bootstrap to be valid?\n\nIt seems that is must be, but in 20 minutes of Googling I have come up empty on a proof.","confidence":0.72,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"bootstrap/180904016pdf_3.txt","reverseDid":"bootstrap/180904016pdf_3.txt","forwardChoice":"bootstrap/180904016pdf_3.txt","reverseChoice":"bootstrap/180904016pdf_3.txt"},{"dataset":"bright-economics","qid":"98","query":"Can Difference-in-Difference be used when the treatment effects get smaller with time since treatment?\n\nRecently, there is an emerging line of the study said that the traditional two-way fixed effect(TWFE) is failed in a lot of case because of the heterogeneous effects of laws over time, follow some paper Goodman-Bacon (2019), Chaisemartin, 2020.\n\nEspecially, Goodman, 2019 did a great job to decompose the single post-treatment dummy. In his note, he answered the question \"Is DD wrong\":\n\nNot in general. The DD research design—comparing outcomes for groups whose treatment status changes to groups whose treatment status does not change—still can be a good idea. The DD specification—estimating the coefficient a single post-treatment dummy—is a bad idea when your treatment effects vary over time (get bigger with time since treatment). In this case, just summarize your findings in a different way—event-study or a linear trend-break, for instance.\n\nMy question here is, if I expect and argue that the treatment effects get smaller with time since treatment, so whether DD specification now is a bad idea?","confidence":0.82,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"treatment_difference/soyouvebeentolddd109_1.txt","reverseDid":"treatment_difference/soyouvebeentolddd109_1.txt","forwardChoice":"treatment_difference/soyouvebeentolddd109_1.txt","reverseChoice":"treatment_difference/soyouvebeentolddd109_1.txt"},{"dataset":"csn-python","qid":"1","query":"Post the content of a URL via sending a HTTP POST request.\n\n    Args:\n        url: A URL.\n        headers: Request headers used by the client.\n        decoded: Whether decode the response body using UTF-8 or the charset specified in Content-Type.\n\n    Returns:\n        The content as a string.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L457-L501","reverseDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L457-L501","forwardChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L457-L501","reverseChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L457-L501"},{"dataset":"csn-python","qid":"109","query":"Extract error code from ftp exception","confidence":0.49,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/ftp_sensor.py#L69-L76","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/ftp_sensor.py#L69-L76","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/ftp_sensor.py#L69-L76","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/ftp_sensor.py#L69-L76"},{"dataset":"csn-python","qid":"11","query":"Uploads the file to Google cloud storage","confidence":0.87,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_gcs.py#L68-L82","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_gcs.py#L68-L82","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_gcs.py#L68-L82","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_gcs.py#L68-L82"},{"dataset":"csn-python","qid":"110","query":"Returns the state of a TaskInstance at the command line.\n    >>> airflow task_state tutorial sleep 2015-01-01\n    success","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L554-L563","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L554-L563","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L554-L563","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/bin/cli.py#L554-L563"},{"dataset":"csn-python","qid":"112","query":"Gets the max partition for a table.\n\n    :param schema: The hive schema the table lives in\n    :type schema: str\n    :param table: The hive table you are interested in, supports the dot\n        notation as in \"my_database.my_table\", if a dot is found,\n        the schema param is disregarded\n    :type table: str\n    :param metastore_conn_id: The hive connection you are interested in.\n        If your default is set you don't need to use this parameter.\n    :type metastore_conn_id: str\n    :param filter_map: partition_key:partition_value map used for partition filtering,\n                       e.g. {'key1': 'value1', 'key2': 'value2'}.\n                       Only partitions matching all partition_key:partition_value\n                       pairs will be considered as candidates of max partition.\n    :type filter_map: map\n    :param field: the field to get the max value from. If there's only\n        one partition field, this will be inferred\n    :type field: str\n\n    >>> max_partition('airflow.static_babynames_partitioned')\n    '2015-01-01'","confidence":0.79,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/hive.py#L23-L55","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/hive.py#L23-L55","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/hive.py#L23-L55","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/hive.py#L23-L55"},{"dataset":"csn-python","qid":"118","query":"Immediately runs the task (without checking or changing db state\n        before execution) and then sets the appropriate final state after\n        completion and runs any post-execute callbacks. Meant to be called\n        only after another function changes the state to running.\n\n        :param mark_success: Don't run the task, mark its state as success\n        :type mark_success: bool\n        :param test_mode: Doesn't record success or failure in the DB\n        :type test_mode: bool\n        :param pool: specifies the pool to use to run the task instance\n        :type pool: str","confidence":0.66,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L825-L943","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L825-L943","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L825-L943","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L825-L943"},{"dataset":"csn-python","qid":"119","query":"Retrieve Cloud SQL Proxy runner. It is used to manage the proxy\n        lifecycle per task.\n\n        :return: The Cloud SQL Proxy runner.\n        :rtype: CloudSqlProxyRunner","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L943-L959","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L943-L959","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L943-L959","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L943-L959"},{"dataset":"csn-python","qid":"122","query":"Insert a list of new documents into an existing collection in the CosmosDB database.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L208-L224","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L208-L224","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L208-L224","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L208-L224"},{"dataset":"csn-python","qid":"128","query":"Do whatever it takes to actually log the specified logging record\n        :param message: message to log","confidence":0.39,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/logging_mixin.py#L92-L102","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/logging_mixin.py#L92-L102","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/logging_mixin.py#L92-L102","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/logging_mixin.py#L92-L102"},{"dataset":"csn-python","qid":"131","query":"Parse DAG files in a loop controlled by DagParsingSignal.\n        Actual DAG parsing loop will run once upon receiving one\n        agent heartbeat message and will report done when finished the loop.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L856-L898","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L856-L898","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L856-L898","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L856-L898"},{"dataset":"csn-python","qid":"133","query":"Returns a Hive thrift client.","confidence":0.64,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L489-L524","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L489-L524","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L489-L524","reverseChoice":"none"},{"dataset":"csn-python","qid":"134","query":"Yields a dependency status that indicate whether the given task instance's trigger\n        rule was met.\n\n        :param ti: the task instance to evaluate the trigger rule of\n        :type ti: airflow.models.TaskInstance\n        :param successes: Number of successful upstream tasks\n        :type successes: bool\n        :param skipped: Number of skipped upstream tasks\n        :type skipped: bool\n        :param failed: Number of failed upstream tasks\n        :type failed: bool\n        :param upstream_failed: Number of upstream_failed upstream tasks\n        :type upstream_failed: bool\n        :param done: Number of completed upstream tasks\n        :type done: bool\n        :param flag_upstream_failed: This is a hack to generate\n            the upstream_failed state creation while checking to see\n            whether the task instance is runnable. It was the shortest\n            path to add the feature\n        :type flag_upstream_failed: bool\n        :param session: database session\n        :type session: sqlalchemy.orm.session.Session","confidence":0.76,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/trigger_rule_dep.py#L91-L224","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/trigger_rule_dep.py#L91-L224","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/trigger_rule_dep.py#L91-L224","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/trigger_rule_dep.py#L91-L224"},{"dataset":"csn-python","qid":"135","query":"Decorates function to execute function at the same time submitting action_logging\n    but in CLI context. It will call action logger callbacks twice,\n    one for pre-execution and the other one for post-execution.\n\n    Action logger will be called with below keyword parameters:\n        sub_command : name of sub-command\n        start_datetime : start datetime instance by utc\n        end_datetime : end datetime instance by utc\n        full_command : full command line arguments\n        user : current user\n        log : airflow.models.log.Log ORM instance\n        dag_id : dag id (optional)\n        task_id : task_id (optional)\n        execution_date : execution date (optional)\n        error : exception instance if there's an exception\n\n    :param f: function instance\n    :return: wrapped function","confidence":0.91,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/cli.py#L36-L81","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/cli.py#L36-L81","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/cli.py#L36-L81","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/cli.py#L36-L81"},{"dataset":"csn-python","qid":"151","query":"Return true if the ticket cache contains \"conf\" information as is found\n    in ticket caches of Kerberos 1.8.1 or later. This is incompatible with the\n    Sun Java Krb5LoginModule in Java6, so we need to take an action to work\n    around it.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/security/kerberos.py#L100-L110","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/security/kerberos.py#L100-L110","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/security/kerberos.py#L100-L110","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/security/kerberos.py#L100-L110"},{"dataset":"csn-python","qid":"154","query":"Upload a file to Azure Data Lake.\n\n        :param local_path: local path. Can be single file, directory (in which case,\n            upload recursively) or glob pattern. Recursive glob patterns using `**`\n            are not supported.\n        :type local_path: str\n        :param remote_path: Remote path to upload to; if multiple files, this is the\n            directory root to write within.\n        :type remote_path: str\n        :param nthreads: Number of threads to use. If None, uses the number of cores.\n        :type nthreads: int\n        :param overwrite: Whether to forcibly overwrite existing files/directories.\n            If False and remote path is a directory, will quit regardless if any files\n            would be overwritten or not. If True, only matching filenames are actually\n            overwritten.\n        :type overwrite: bool\n        :param buffersize: int [2**22]\n            Number of bytes for internal buffer. This block cannot be bigger than\n            a chunk and cannot be smaller than a block.\n        :type buffersize: int\n        :param blocksize: int [2**22]\n            Number of bytes for a block. Within each chunk, we write a smaller\n            block for each API call. This block cannot be bigger than a chunk.\n        :type blocksize: int","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L70-L104","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L70-L104","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L70-L104","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L70-L104"},{"dataset":"csn-python","qid":"155","query":"Writes the log to the remote_log_location. Fails silently if no hook\n        was created.\n        :param log: the log to write to the remote_log_location\n        :type log: str\n        :param remote_log_location: the log's location in remote storage\n        :type remote_log_location: str (path)\n        :param append: if False, any existing log file is overwritten. If True,\n            the new log is appended to any existing logs.\n        :type append: bool","confidence":0.44,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/wasb_task_handler.py#L154-L178","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/s3_task_handler.py#L146-L170","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/wasb_task_handler.py#L154-L178","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/s3_task_handler.py#L146-L170"},{"dataset":"csn-python","qid":"156","query":"Checks that a prefix exists in a bucket\n\n        :param bucket_name: the name of the bucket\n        :type bucket_name: str\n        :param prefix: a key prefix\n        :type prefix: str\n        :param delimiter: the delimiter marks key hierarchy.\n        :type delimiter: str","confidence":0.9,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L92-L107","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L92-L107","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L92-L107","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L92-L107"},{"dataset":"csn-python","qid":"161","query":"Test if a container group exists\n\n        :param resource_group: the name of the resource group\n        :type resource_group: str\n        :param name: the name of the container group\n        :type name: str","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_container_instance_hook.py#L160-L172","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_container_instance_hook.py#L160-L172","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_container_instance_hook.py#L160-L172","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_container_instance_hook.py#L160-L172"},{"dataset":"csn-python","qid":"162","query":"Read logs of given task instance from local machine.\n        :param task_instance: task instance object\n        :param try_number: task instance try_number to read logs from. If None\n                           it returns all logs separated by try_number\n        :param metadata: log metadata,\n                         can be used for steaming log reading and auto-tailing.\n        :return: a list of logs","confidence":0.47,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_task_handler.py#L135-L168","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_task_handler.py#L135-L168","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_task_handler.py#L135-L168","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_task_handler.py#L135-L168"},{"dataset":"csn-python","qid":"166","query":"Checks whether a partition with a given name exists\n\n        :param schema: Name of hive schema (database) @table belongs to\n        :type schema: str\n        :param table: Name of hive table @partition belongs to\n        :type schema: str\n        :partition: Name of the partitions to check for (eg `a=b/c=d`)\n        :type schema: str\n        :rtype: bool\n\n        >>> hh = HiveMetastoreHook()\n        >>> t = 'static_babynames_partitioned'\n        >>> hh.check_for_named_partition('airflow', t, \"ds=2015-01-01\")\n        True\n        >>> hh.check_for_named_partition('airflow', t, \"ds=xxx\")\n        False","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L556-L576","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L556-L576","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L556-L576","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L556-L576"},{"dataset":"csn-python","qid":"170","query":"Downloads mail's attachments in the mail folder by its name to the local directory.\n\n        :param name: The name of the attachment that will be downloaded.\n        :type name: str\n        :param local_output_directory: The output directory on the local machine\n                                       where the files will be downloaded to.\n        :type local_output_directory: str\n        :param mail_folder: The mail folder where to look at.\n        :type mail_folder: str\n        :param check_regex: Checks the name for a regular expression.\n        :type check_regex: bool\n        :param latest_only: If set to True it will only download\n                            the first matched attachment.\n        :type latest_only: bool\n        :param not_found_mode: Specify what should happen if no attachment has been found.\n                               Supported values are 'raise', 'warn' and 'ignore'.\n                               If it is set to 'raise' it will raise an exception,\n                               if set to 'warn' it will only print a warning and\n                               if set to 'ignore' it won't notify you at all.\n        :type not_found_mode: str","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L104-L141","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L104-L141","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L104-L141","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L104-L141"},{"dataset":"csn-python","qid":"174","query":":param dag_id: DAG ID\n        :type dag_id: unicode\n        :param execution_date: execution date\n        :type execution_date: datetime\n        :return: DagRun corresponding to the given dag_id and execution date\n            if one exists. None otherwise.\n        :rtype: airflow.models.DagRun","confidence":0.23,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagrun.py#L392-L407","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagrun.py#L392-L407","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagrun.py#L392-L407","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagrun.py#L392-L407"},{"dataset":"csn-python","qid":"180","query":"Create X-axis","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L419-L448","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L419-L448","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L419-L448","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L419-L448"},{"dataset":"csn-python","qid":"184","query":"Check if table exists\n\n        >>> hh = HiveMetastoreHook()\n        >>> hh.table_exists(db='airflow', table_name='static_babynames')\n        True\n        >>> hh.table_exists(db='airflow', table_name='does_not_exist')\n        False","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L731-L745","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L731-L745","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L731-L745","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L731-L745"},{"dataset":"csn-python","qid":"186","query":"Prints a report around DagBag loading stats","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L398-L416","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L398-L416","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L398-L416","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L398-L416"},{"dataset":"csn-python","qid":"191","query":"Returns a snowflake.connection object","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/snowflake_hook.py#L107-L113","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/snowflake_hook.py#L107-L113","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/snowflake_hook.py#L107-L113","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/snowflake_hook.py#L107-L113"},{"dataset":"csn-python","qid":"199","query":"Gets the content of a URL via sending a HTTP GET request.\n\n    Args:\n        url: A URL.\n        headers: Request headers used by the client.\n        decoded: Whether decode the response body using UTF-8 or the charset specified in Content-Type.\n\n    Returns:\n        The content as a string.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L415-L454","reverseDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L415-L454","forwardChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L415-L454","reverseChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L415-L454"},{"dataset":"csn-python","qid":"204","query":"This method checks whether a new DagRun needs to be created\n        for a DAG based on scheduling interval.\n        Returns DagRun if one is scheduled. Otherwise returns None.","confidence":0.64,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L766-L894","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L766-L894","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L766-L894","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L766-L894"},{"dataset":"csn-python","qid":"205","query":"This function checks if there are any tasks in the dagrun (or all)\n        that have a scheduled state but are not known by the\n        executor. If it finds those it will reset the state to None\n        so they will get picked up again.\n        The batch option is for performance reasons as the queries are made in\n        sequence.\n\n        :param filter_by_dag_run: the dag_run we want to process, None if all\n        :type filter_by_dag_run: airflow.models.DagRun\n        :return: the TIs reset (in expired SQLAlchemy state)\n        :rtype: list[airflow.models.TaskInstance]","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L223-L298","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L223-L298","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L223-L298","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L223-L298"},{"dataset":"csn-python","qid":"206","query":"Respond to executor events.","confidence":0.83,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1442-L1484","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1442-L1484","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1442-L1484","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1442-L1484"},{"dataset":"csn-python","qid":"208","query":"Creates new instance.\n\n        :type instance_id: str\n        :param instance_id: The ID for the new instance.\n        :type main_cluster_id: str\n        :param main_cluster_id: The ID for main cluster for the new instance.\n        :type main_cluster_zone: str\n        :param main_cluster_zone: The zone for main cluster.\n            See https://cloud.google.com/bigtable/docs/locations for more details.\n        :type project_id: str\n        :param project_id: Optional, Google Cloud Platform project ID where the\n            BigTable exists. If set to None or missing,\n            the default project_id from the GCP connection is used.\n        :type replica_cluster_id: str\n        :param replica_cluster_id: (optional) The ID for replica cluster for the new\n            instance.\n        :type replica_cluster_zone: str\n        :param replica_cluster_zone: (optional)  The zone for replica cluster.\n        :type instance_type: enums.Instance.Type\n        :param instance_type: (optional) The type of the instance.\n        :type instance_display_name: str\n        :param instance_display_name: (optional) Human-readable name of the instance.\n                Defaults to ``instance_id``.\n        :type instance_labels: dict\n        :param instance_labels: (optional) Dictionary of labels to associate with the\n            instance.\n        :type cluster_nodes: int\n        :param cluster_nodes: (optional) Number of nodes for cluster.\n        :type cluster_storage_type: enums.StorageType\n        :param cluster_storage_type: (optional) The type of storage.\n        :type timeout: int\n        :param timeout: (optional) timeout (in seconds) for instance creation.\n                        If None is not specified, Operator will wait indefinitely.","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_bigtable_hook.py#L90-L168","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_bigtable_hook.py#L90-L168","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_bigtable_hook.py#L90-L168","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_bigtable_hook.py#L90-L168"},{"dataset":"csn-python","qid":"211","query":"Synthesizes text input\n\n        :param input_data: text input to be synthesized. See more:\n            https://googleapis.github.io/google-cloud-python/latest/texttospeech/gapic/v1/types.html#google.cloud.texttospeech_v1.types.SynthesisInput\n        :type input_data: dict or google.cloud.texttospeech_v1.types.SynthesisInput\n        :param voice: configuration of voice to be used in synthesis. See more:\n            https://googleapis.github.io/google-cloud-python/latest/texttospeech/gapic/v1/types.html#google.cloud.texttospeech_v1.types.VoiceSelectionParams\n        :type voice: dict or google.cloud.texttospeech_v1.types.VoiceSelectionParams\n        :param audio_config: configuration of the synthesized audio. See more:\n            https://googleapis.github.io/google-cloud-python/latest/texttospeech/gapic/v1/types.html#google.cloud.texttospeech_v1.types.AudioConfig\n        :type audio_config: dict or google.cloud.texttospeech_v1.types.AudioConfig\n        :return: SynthesizeSpeechResponse See more:\n            https://googleapis.github.io/google-cloud-python/latest/texttospeech/gapic/v1/types.html#google.cloud.texttospeech_v1.types.SynthesizeSpeechResponse\n        :rtype: object\n        :param retry: (Optional) A retry object used to retry requests. If None is specified,\n                requests will not be retried.\n        :type retry: google.api_core.retry.Retry\n        :param timeout: (Optional) The amount of time, in seconds, to wait for the request to complete.\n            Note that if retry is specified, the timeout applies to each individual attempt.\n        :type timeout: float","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_text_to_speech_hook.py#L53-L80","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_text_to_speech_hook.py#L53-L80","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_text_to_speech_hook.py#L53-L80","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_text_to_speech_hook.py#L53-L80"},{"dataset":"csn-python","qid":"212","query":"Inserts instance template using body specified\n        Must be called with keyword arguments rather than positional.\n\n        :param body: Instance template representation as object according to\n            https://cloud.google.com/compute/docs/reference/rest/v1/instanceTemplates\n        :type body: dict\n        :param request_id: Optional, unique request_id that you might add to achieve\n            full idempotence (for example when client call times out repeating the request\n            with the same request id will not create a new instance template again)\n            It should be in UUID format as defined in RFC 4122\n        :type request_id: str\n        :param project_id: Optional, Google Cloud Platform project ID where the\n            Compute Engine Instance exists. If set to None or missing,\n            the default project_id from the GCP connection is used.\n        :type project_id: str\n        :return: None","confidence":0.83,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_compute_hook.py#L189-L220","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_compute_hook.py#L189-L220","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_compute_hook.py#L189-L220","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_compute_hook.py#L189-L220"},{"dataset":"csn-python","qid":"222","query":"Gets details of specified cluster\n\n        :param name: The name of the cluster to retrieve\n        :type name: str\n        :param project_id: Google Cloud Platform project ID\n        :type project_id: str\n        :param retry: A retry object used to retry requests. If None is specified,\n            requests will not be retried.\n        :type retry: google.api_core.retry.Retry\n        :param timeout: The amount of time, in seconds, to wait for the request to\n            complete. Note that if retry is specified, the timeout applies to each\n            individual attempt.\n        :type timeout: float\n        :return: google.cloud.container_v1.types.Cluster","confidence":0.88,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L221-L247","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L221-L247","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L221-L247","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L221-L247"},{"dataset":"csn-python","qid":"226","query":"Coerces content or all values of content if it is a dict to a string. The\n    function will throw if content contains non-string or non-numeric types.\n\n    The reason why we have this function is because the ``self.json`` field must be a\n    dict with only string values. This is because ``render_template`` will fail\n    for numerical values.","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/databricks_operator.py#L34-L58","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/databricks_operator.py#L34-L58","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/databricks_operator.py#L34-L58","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/databricks_operator.py#L34-L58"},{"dataset":"csn-python","qid":"227","query":"Waits for the named operation to complete - checks status of the\n        asynchronous call.\n\n        :param project_id: Project ID of the project that contains the instance.\n        :type project_id: str\n        :param operation_name: Name of the operation.\n        :type operation_name: str\n        :return: None","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L343-L368","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L343-L368","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L343-L368","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L343-L368"},{"dataset":"csn-python","qid":"229","query":"Copies an object from a bucket to another, with renaming if requested.\n\n        destination_bucket or destination_object can be omitted, in which case\n        source bucket/object is used, but not both.\n\n        :param source_bucket: The bucket of the object to copy from.\n        :type source_bucket: str\n        :param source_object: The object to copy.\n        :type source_object: str\n        :param destination_bucket: The destination of the object to copied to.\n            Can be omitted; then the same bucket is used.\n        :type destination_bucket: str\n        :param destination_object: The (renamed) path of the object if given.\n            Can be omitted; then the same name is used.\n        :type destination_object: str","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L54-L96","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L54-L96","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L54-L96","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L54-L96"},{"dataset":"csn-python","qid":"232","query":"Construct the Discord JSON payload. All relevant parameters are combined here\n        to a valid Discord JSON payload.\n\n        :return: Discord payload (str) to send","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/discord_webhook_hook.py#L102-L124","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/discord_webhook_hook.py#L102-L124","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/discord_webhook_hook.py#L102-L124","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/discord_webhook_hook.py#L102-L124"},{"dataset":"csn-python","qid":"233","query":"Fetches the operation from Google Cloud\n\n        :param operation_name: Name of operation to fetch\n        :type operation_name: str\n        :param project_id: Google Cloud Platform project ID\n        :type project_id: str\n        :return: The new, updated operation from Google Cloud","confidence":0.81,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L96-L108","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L96-L108","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L96-L108","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L96-L108"},{"dataset":"csn-python","qid":"236","query":"Performs the request\n\n        :param endpoint: the endpoint to be called i.e. resource/v1/query?\n        :type endpoint: str\n        :param data: payload to be uploaded or request parameters\n        :type data: dict\n        :param headers: additional headers to be passed through as a dictionary\n        :type headers: dict\n        :param extra_options: additional options to be used when executing the request\n            i.e. {'check_response': False} to avoid checking raising exceptions on non\n            2XX or 3XX status codes\n        :type extra_options: dict","confidence":0.79,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L85-L131","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L85-L131","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L85-L131","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L85-L131"},{"dataset":"csn-python","qid":"24","query":"Updates a database resource inside a Cloud SQL instance.\n\n        This method supports patch semantics.\n        See https://cloud.google.com/sql/docs/mysql/admin-api/how-tos/performance#patch.\n\n        :param instance: Database instance ID. This does not include the project ID.\n        :type instance: str\n        :param database: Name of the database to be updated in the instance.\n        :type database: str\n        :param body: The request body, as described in\n            https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases/insert#request-body.\n        :type body: dict\n        :param project_id: Project ID of the project that contains the instance. If set\n            to None or missing, the default project_id from the GCP connection is used.\n        :type project_id: str\n        :return: None","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L229-L256","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L229-L256","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L229-L256","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L229-L256"},{"dataset":"csn-python","qid":"244","query":"Clears import errors for files that no longer exist.\n\n        :param session: session for ORM operations\n        :type session: sqlalchemy.orm.session.Session","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L931-L944","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L931-L944","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L931-L944","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L931-L944"},{"dataset":"csn-python","qid":"247","query":"Executes command received and stores result state in queue.\n        :param key: the key to identify the TI\n        :type key: tuple(dag_id, task_id, execution_date)\n        :param command: the command to execute\n        :type command: str","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/local_executor.py#L73-L92","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/local_executor.py#L73-L92","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/local_executor.py#L73-L92","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/local_executor.py#L73-L92"},{"dataset":"csn-python","qid":"254","query":"Returns a list of files on the remote system.\n        :param path: full path to the remote directory to list\n        :type path: str","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sftp_hook.py#L145-L153","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sftp_hook.py#L145-L153","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sftp_hook.py#L145-L153","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sftp_hook.py#L145-L153"},{"dataset":"csn-python","qid":"255","query":"Set the dag run for a specific execution date to running.\n    :param dag: the DAG of which to alter state\n    :param execution_date: the execution date from which to start looking\n    :param commit: commit DAG and tasks to be altered to the database\n    :param session: database session\n    :return: If commit is true, list of tasks that have been updated,\n             otherwise list of tasks that will be updated","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L284-L303","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L284-L303","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L284-L303","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L284-L303"},{"dataset":"csn-python","qid":"26","query":"Allocate IDs for incomplete keys.\n\n        .. seealso::\n            https://cloud.google.com/datastore/docs/reference/rest/v1/projects/allocateIds\n\n        :param partial_keys: a list of partial keys.\n        :type partial_keys: list\n        :return: a list of full keys.\n        :rtype: list","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L62-L81","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L62-L81","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L62-L81","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L62-L81"},{"dataset":"csn-python","qid":"264","query":"Construct the command to poll the driver status.\n\n        :return: full command to be executed","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L299-L320","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L299-L320","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L299-L320","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L299-L320"},{"dataset":"csn-python","qid":"268","query":"Converts a tuple to RECORD that contains n fields, each will be converted\n        to its corresponding data type in bq and will be named 'field_<index>', where\n        index is determined by the order of the tuple elements defined in cassandra.","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/cassandra_to_gcs.py#L258-L266","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/cassandra_to_gcs.py#L258-L266","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/cassandra_to_gcs.py#L258-L266","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/cassandra_to_gcs.py#L258-L266"},{"dataset":"csn-python","qid":"269","query":"Checks if an blob_name is updated in Google Cloud Storage.\n\n        :param bucket_name: The Google cloud storage bucket where the object is.\n        :type bucket_name: str\n        :param object_name: The name of the object to check in the Google cloud\n            storage bucket.\n        :type object_name: str\n        :param ts: The timestamp to check against.\n        :type ts: datetime.datetime","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L223-L253","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L223-L253","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L223-L253","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcs_hook.py#L223-L253"},{"dataset":"csn-python","qid":"270","query":"Parse DAG files repeatedly in a standalone loop.","confidence":0.33,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L856-L898","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L810-L854","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L856-L898","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L810-L854"},{"dataset":"csn-python","qid":"272","query":"Configure a csv writer with the file_handle and write schema\n        as headers for the new file.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/mysql_to_gcs.py#L201-L208","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/mysql_to_gcs.py#L201-L208","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/mysql_to_gcs.py#L201-L208","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/mysql_to_gcs.py#L201-L208"},{"dataset":"csn-python","qid":"273","query":"Get a list of all available fields for an object.\n\n        :param obj: The name of the Salesforce object that we are getting a description of.\n        :type obj: str\n        :return: the names of the fields.\n        :rtype: list of str","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/salesforce_hook.py#L110-L123","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/salesforce_hook.py#L110-L123","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/salesforce_hook.py#L110-L123","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/salesforce_hook.py#L110-L123"},{"dataset":"csn-python","qid":"274","query":"Check whether a potential object is a subclass of\n    the AirflowPlugin class.\n\n    :param plugin_obj: potential subclass of AirflowPlugin\n    :param existing_plugins: Existing list of AirflowPlugin subclasses\n    :return: Whether or not the obj is a valid subclass of\n        AirflowPlugin","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L101-L118","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L101-L118","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L101-L118","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L101-L118"},{"dataset":"csn-python","qid":"278","query":"Delete pool by a given name.","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L70-L82","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L70-L82","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L70-L82","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L70-L82"},{"dataset":"csn-python","qid":"282","query":"Get the very latest state from the database, if a session is passed,\n        we use and looking up the state becomes part of the session, otherwise\n        a new session is used.","confidence":0.5,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L424-L455","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L424-L455","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L424-L455","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L424-L455"},{"dataset":"csn-python","qid":"286","query":"Returns whether or not all the conditions are met for this task instance to be run\n        given the context for the dependencies (e.g. a task instance being force run from\n        the UI will ignore some dependencies).\n\n        :param dep_context: The execution context that determines the dependencies that\n            should be evaluated.\n        :type dep_context: DepContext\n        :param session: database session\n        :type session: sqlalchemy.orm.session.Session\n        :param verbose: whether log details on failed dependencies on\n            info or debug log level\n        :type verbose: bool","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L550-L586","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L550-L586","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L550-L586","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskinstance.py#L550-L586"},{"dataset":"csn-python","qid":"287","query":"Creates a cluster, consisting of the specified number and type of Google Compute\n        Engine instances.\n\n        :param cluster: A Cluster protobuf or dict. If dict is provided, it must\n            be of the same form as the protobuf message\n            :class:`google.cloud.container_v1.types.Cluster`\n        :type cluster: dict or google.cloud.container_v1.types.Cluster\n        :param project_id: Google Cloud Platform project ID\n        :type project_id: str\n        :param retry: A retry object (``google.api_core.retry.Retry``) used to\n            retry requests.\n            If None is specified, requests will not be retried.\n        :type retry: google.api_core.retry.Retry\n        :param timeout: The amount of time, in seconds, to wait for the request to\n            complete. Note that if retry is specified, the timeout applies to each\n            individual attempt.\n        :type timeout: float\n        :return: The full url to the new, or existing, cluster\n        :raises:\n            ParseError: On JSON parsing problems when trying to convert dict\n            AirflowException: cluster is not dict type nor Cluster proto type","confidence":0.92,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L170-L219","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L170-L219","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L170-L219","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_container_hook.py#L170-L219"},{"dataset":"csn-python","qid":"292","query":"Sets the environment variable `GOOGLE_APPLICATION_CREDENTIALS` with either:\n\n        - The path to the keyfile from the specified connection id\n        - A generated file's path if the user specified JSON in the connection id. The\n            file is assumed to be deleted after the process dies due to how mkstemp()\n            works.\n\n        The environment variable is used inside the gcloud command to determine correct\n        service account to use.","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/gcp_container_operator.py#L282-L308","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/gcp_container_operator.py#L282-L308","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/gcp_container_operator.py#L282-L308","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/gcp_container_operator.py#L282-L308"},{"dataset":"csn-python","qid":"297","query":"How many Celery tasks should be sent to each worker process.\n\n        :return: Number of tasks that should be used per process\n        :rtype: int","confidence":0.88,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/celery_executor.py#L168-L176","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/celery_executor.py#L168-L176","forwardChoice":"none","reverseChoice":"none"},{"dataset":"csn-python","qid":"3","query":"Scans through a string for substrings matched some patterns.\n\n    Args:\n        text: A string to be scanned.\n        patterns: a list of regex pattern.\n\n    Returns:\n        a list if matched. empty if not.","confidence":0.87,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L252-L268","reverseDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L252-L268","forwardChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L252-L268","reverseChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L252-L268"},{"dataset":"csn-python","qid":"30","query":"Given a number of tasks, builds a dependency chain.\n\n    chain(task_1, task_2, task_3, task_4)\n\n    is equivalent to\n\n    task_1.set_downstream(task_2)\n    task_2.set_downstream(task_3)\n    task_3.set_downstream(task_4)","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L153-L166","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L153-L166","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L153-L166","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L153-L166"},{"dataset":"csn-python","qid":"301","query":"Updates the counters per state of the tasks that were running. Can re-add\n        to tasks to run in case required.\n\n        :param ti_status: the internal status of the backfill job tasks\n        :type ti_status: BackfillJob._DagRunTaskStatus","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1929-L1977","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1929-L1977","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1929-L1977","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1929-L1977"},{"dataset":"csn-python","qid":"302","query":"Download a file from Azure File Share.\n\n        :param stream: A filehandle to store the file to.\n        :type stream: file-like object\n        :param share_name: Name of the share.\n        :type share_name: str\n        :param directory_name: Name of the directory.\n        :type directory_name: str\n        :param file_name: Name of the file.\n        :type file_name: str\n        :param kwargs: Optional keyword arguments that\n            `FileService.get_file_to_stream()` takes.\n        :type kwargs: object","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_fileshare_hook.py#L136-L153","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_fileshare_hook.py#L136-L153","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_fileshare_hook.py#L136-L153","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_fileshare_hook.py#L136-L153"},{"dataset":"csn-python","qid":"315","query":"Cancels an transfer operation in Google Storage Transfer Service.\n\n        :param operation_name: Name of the transfer operation.\n        :type operation_name: str\n        :rtype: None","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L235-L243","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L235-L243","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L235-L243","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L235-L243"},{"dataset":"csn-python","qid":"318","query":"Gets a Model. Blocks until finished.","confidence":0.77,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L257-L273","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L257-L273","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L257-L273","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L257-L273"},{"dataset":"csn-python","qid":"319","query":"Utility function to perform an API call with retries\n\n        :param endpoint_info: Tuple of method and endpoint\n        :type endpoint_info: tuple[string, string]\n        :param json: Parameters for this API call.\n        :type json: dict\n        :return: If the api call returns a OK status code,\n            this function returns the response in JSON. Otherwise,\n            we throw an AirflowException.\n        :rtype: dict","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/databricks_hook.py#L99-L154","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/databricks_hook.py#L99-L154","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/databricks_hook.py#L99-L154","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/databricks_hook.py#L99-L154"},{"dataset":"csn-python","qid":"320","query":"Parses some DatabaseError to provide a better error message","confidence":0.53,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/presto_hook.py#L67-L78","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/presto_hook.py#L101-L118","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/presto_hook.py#L67-L78","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/presto_hook.py#L101-L118"},{"dataset":"csn-python","qid":"321","query":"Retrieves connection to Cloud Vision.\n\n        :return: Google Cloud Vision client object.\n        :rtype: google.cloud.vision_v1.ProductSearchClient","confidence":0.69,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_vision_hook.py#L106-L115","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_vision_hook.py#L106-L115","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_vision_hook.py#L106-L115","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_vision_hook.py#L106-L115"},{"dataset":"csn-python","qid":"322","query":"Source: Android mobile","confidence":0.75,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/showroom.py#L11-L24","reverseDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/showroom.py#L11-L24","forwardChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/showroom.py#L11-L24","reverseChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/showroom.py#L11-L24"},{"dataset":"csn-python","qid":"329","query":"A generic way to insert a set of tuples into a table,\n        the whole set of inserts is treated as one transaction\n        Changes from standard DbApiHook implementation:\n\n        - Oracle SQL queries in cx_Oracle can not be terminated with a semicolon (`;`)\n        - Replace NaN values with NULL using `numpy.nan_to_num` (not using\n          `is_nan()` because of input types error for strings)\n        - Coerce datetime cells to Oracle DATETIME format during insert\n\n        :param table: target Oracle table, use dot notation to target a\n            specific database\n        :type table: str\n        :param rows: the rows to insert into the table\n        :type rows: iterable of tuples\n        :param target_fields: the names of the columns to fill in the table\n        :type target_fields: iterable of str\n        :param commit_every: the maximum number of rows to insert in one transaction\n            Default 1000, Set greater than 0.\n            Set 1 to insert each row in each single transaction\n        :type commit_every: int","confidence":0.57,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L117-L182","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L117-L182","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L117-L182","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L117-L182"},{"dataset":"csn-python","qid":"337","query":"Delete all DB records related to the specified Dag.","confidence":0.47,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L98-L109","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L98-L109","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L98-L109","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L98-L109"},{"dataset":"csn-python","qid":"338","query":"r\"\"\"SqueezeNet 1.1 model from the `official SqueezeNet repo\n    <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.\n    SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters\n    than SqueezeNet 1.0, without sacrificing accuracy.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet","confidence":0.74,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/squeezenet.py#L117-L129","reverseDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/squeezenet.py#L117-L129","forwardChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/squeezenet.py#L117-L129","reverseChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/squeezenet.py#L117-L129"},{"dataset":"csn-python","qid":"351","query":"A role that allows you to include a list of template fields in the middle of the text. This is especially\n    useful when writing guides describing how to use the operator.\n    The result is a list of fields where each field is shorted in the literal block.\n\n    Sample usage::\n\n    :template-fields:`airflow.contrib.operators.gcp_natural_language_operator.CloudLanguageAnalyzeSentimentOperator`\n\n    For further information look at:\n\n    * [http://docutils.sourceforge.net/docs/howto/rst-roles.html](Creating reStructuredText Interpreted\n      Text Roles)","confidence":0.61,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/docs/exts/docroles.py#L58-L88","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/docs/exts/docroles.py#L58-L88","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/docs/exts/docroles.py#L58-L88","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/docs/exts/docroles.py#L58-L88"},{"dataset":"csn-python","qid":"352","query":"Returns a Redis connection.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redis_hook.py#L45-L66","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redis_hook.py#L45-L66","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redis_hook.py#L45-L66","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redis_hook.py#L45-L66"},{"dataset":"csn-python","qid":"357","query":"Get a list of num_components batchwise probabilities.","confidence":0.7,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/mixture.py#L497-L502","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/mixture.py#L497-L502","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/mixture.py#L497-L502","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/mixture.py#L497-L502"},{"dataset":"csn-python","qid":"358","query":"Helper method that set dag run state in the DB.\n    :param dag_id: dag_id of target dag run\n    :param execution_date: the execution date from which to start looking\n    :param state: target state\n    :param session: database session","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L188-L207","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L188-L207","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L188-L207","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L188-L207"},{"dataset":"csn-python","qid":"363","query":"A performant bulk insert for cx_Oracle\n        that uses prepared statements via `executemany()`.\n        For best performance, pass in `rows` as an iterator.\n\n        :param table: target Oracle table, use dot notation to target a\n            specific database\n        :type table: str\n        :param rows: the rows to insert into the table\n        :type rows: iterable of tuples\n        :param target_fields: the names of the columns to fill in the table, default None.\n            If None, each rows should have some order as table columns name\n        :type target_fields: iterable of str Or None\n        :param commit_every: the maximum number of rows to insert in one transaction\n            Default 5000. Set greater than 0. Set 1 to insert each row in each transaction\n        :type commit_every: int","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L184-L231","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L184-L231","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L184-L231","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/oracle_hook.py#L184-L231"},{"dataset":"csn-python","qid":"366","query":"Checks the mail folder for mails containing attachments with the given name.\n\n        :param name: The name of the attachment that will be searched for.\n        :type name: str\n        :param mail_folder: The mail folder where to look at.\n        :type mail_folder: str\n        :param check_regex: Checks the name for a regular expression.\n        :type check_regex: bool\n        :returns: True if there is an attachment with the given name and False if not.\n        :rtype: bool","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L49-L66","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L49-L66","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L49-L66","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/imap_hook.py#L49-L66"},{"dataset":"csn-python","qid":"367","query":"Yield successive chunks of a given size from a list of items","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L121-L128","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L121-L128","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L121-L128","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L121-L128"},{"dataset":"csn-python","qid":"376","query":"Tries really hard to terminate all children (including grandchildren). Will send\n    sig (SIGTERM) to the process group of pid. If any process is alive after timeout\n    a SIGKILL will be send.\n\n    :param log: log handler\n    :param pid: pid to kill\n    :param sig: signal type\n    :param timeout: how much time a process has to terminate","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L243-L289","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L243-L289","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L243-L289","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L243-L289"},{"dataset":"csn-python","qid":"378","query":"Cancel all started queries that have not yet completed","confidence":0.6,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1298-L1339","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1298-L1339","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1298-L1339","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1298-L1339"},{"dataset":"csn-python","qid":"384","query":"Send termination signal to DAG parsing processor manager\n        and expect it to terminate all DAG file processors.","confidence":0.31,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L1285-L1320","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L637-L643","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L1285-L1320","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dag_processing.py#L637-L643"},{"dataset":"csn-python","qid":"385","query":"r\"\"\"GoogLeNet (Inception v1) model architecture from\n    `\"Going Deeper with Convolutions\" <http://arxiv.org/abs/1409.4842>`_.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n        aux_logits (bool): If True, adds two auxiliary branches that can improve training.\n            Default: *False* when pretrained is True otherwise *True*\n        transform_input (bool): If True, preprocesses the input according to the method with which it\n            was trained on ImageNet. Default: *False*","confidence":0.88,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/googlenet.py#L18-L47","reverseDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/googlenet.py#L18-L47","forwardChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/googlenet.py#L18-L47","reverseChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/models/googlenet.py#L18-L47"},{"dataset":"csn-python","qid":"39","query":"Retrieves a database resource from a Cloud SQL instance.\n\n        :param instance: Database instance ID. This does not include the project ID.\n        :type instance: str\n        :param database: Name of the database in the instance.\n        :type database: str\n        :param project_id: Project ID of the project that contains the instance. If set\n            to None or missing, the default project_id from the GCP connection is used.\n        :type project_id: str\n        :return: A Cloud SQL database resource, as described in\n            https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases#resource.\n        :rtype: dict","confidence":0.82,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L183-L202","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L183-L202","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L183-L202","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L183-L202"},{"dataset":"csn-python","qid":"390","query":"Create log file and directory if required.\n        :param filename: task instance object\n        :return: relative log path of the given task instance","confidence":0.87,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_processor_handler.py#L129-L149","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_processor_handler.py#L129-L149","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_processor_handler.py#L129-L149","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/log/file_processor_handler.py#L129-L149"},{"dataset":"csn-python","qid":"393","query":"Attempt to import tensorflow, and ensure its version is sufficient.\n\n  Raises:\n    ImportError: if either tensorflow is not importable or its version is\n    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scales.","confidence":0.69,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L882-L896","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L882-L896","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L882-L896","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L882-L896"},{"dataset":"csn-python","qid":"404","query":"Loads the manifest file and register the `url_for_asset_` template tag.\n    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Pass a list of sql\n        statements to the sql parameter to get them to execute\n        sequentially\n\n        :param sql: the sql statement to be executed (str) or a list of\n            sql statements to execute\n        :type sql: str or list\n        :param autocommit: What to set the connection's autocommit setting to\n            before executing the query.\n        :type autocommit: bool\n        :param parameters: The parameters to render the SQL query with.\n        :type parameters: mapping or iterable","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/dbapi_hook.py#L132-L166","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/dbapi_hook.py#L132-L166","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/dbapi_hook.py#L132-L166","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/dbapi_hook.py#L132-L166"},{"dataset":"csn-python","qid":"422","query":"Returns the distribution's required args.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/joint_distribution_sequential.py#L475-L487","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/joint_distribution_sequential.py#L475-L487","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/joint_distribution_sequential.py#L475-L487","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/joint_distribution_sequential.py#L475-L487"},{"dataset":"csn-python","qid":"424","query":"Equivalent to tf.nn.softmax but works around b/70297725.","confidence":0.45,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L958-L972","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L958-L972","forwardChoice":"none","reverseChoice":"none"},{"dataset":"csn-python","qid":"428","query":"Check if the process launched to process this file is done.\n\n        :return: whether the process is finished running\n        :rtype: bool","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L473-L504","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L473-L504","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L473-L504","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L473-L504"},{"dataset":"csn-python","qid":"44","query":"Create connection in the Connection table, according to whether it uses\n        proxy, TCP, UNIX sockets, SSL. 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batch_size: Number of elements in each training batch.\n\n  Returns:\n    batch_features: `Tensor` feed  features, of shape\n      `[batch_size] + x.shape[1:]`.\n    batch_labels: `Tensor` feed of labels, of shape\n      `[batch_size] + 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 If there is no next interceptor, we perform an \"immediate\" call to `func`.\n  That is, `func` terminates without forwarding its execution to another\n  interceptor.\n\n  Args:\n    func: Function to wrap.\n\n  Returns:\n    The decorated 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key: s3 key\n        :type key: str\n        :param bucket: s3 bucket\n        :type bucket: str\n        :return: None","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L134-L155","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L134-L155","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L134-L155","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L134-L155"},{"dataset":"csn-python","qid":"471","query":"Sample the dynamic latent prior.\n\n    Args:\n      samples: Number of samples to draw from the latent distribution.\n      batch_size: Number of sequences to sample.\n      length: Number of timesteps to sample for each sequence.\n      fixed: Boolean for whether or not to share the same random\n        sample across all sequences.\n\n    Returns:\n      A tuple of a sample tensor of shape [samples, batch_size, length\n      latent_size], and a MultivariateNormalDiag distribution from which\n      the tensor was sampled, with event shape [latent_size], and batch\n      shape [samples, 1, length] if fixed or [samples, batch_size,\n      length] 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Setting this to\n            None implies no timeout for any SageMaker job.\n        :type max_ingestion_time: int\n        :return: A response to training job creation","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L325-L366","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L325-L366","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L325-L366","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L325-L366"},{"dataset":"csn-python","qid":"490","query":"Transforms a log-prob function using a bijector.\n\n  This takes a log-prob function and creates a new log-prob function that now\n  takes takes state in the domain of the bijector, forward transforms that state\n  and calls the original log-prob function. It then returns the log-probability\n  that correctly accounts for this transformation.\n\n  The forward-transformed state is pre-pended to the original log-prob\n  function's extra returns and returned as the new extra return.\n\n  For convenience you can also pass the initial state (in the original space),\n  and this function will return the inverse transformed as the 2nd return value.\n  You'd use this to initialize MCMC operators that operate in the transformed\n  space.\n\n  Args:\n    log_prob_fn: Log prob fn.\n    bijector: Bijector(s), must be of the same structure as the `log_prob_fn`\n      inputs.\n    init_state: Initial state, in the original space.\n\n  Returns:\n    transformed_log_prob_fn: Transformed log prob fn.\n    transformed_init_state: If `init_state` is provided. Initial state in the\n      transformed space.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L181-L239","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L181-L239","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L181-L239","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L181-L239"},{"dataset":"csn-python","qid":"499","query":"Process a set of task instances from a set of dag runs. Special handling is done\n        to account for different task instance states that could be present when running\n        them in a backfill process.\n\n        :param ti_status: the internal status of the job\n        :type ti_status: BackfillJob._DagRunTaskStatus\n        :param executor: the executor to run the task instances\n        :type executor: BaseExecutor\n        :param pickle_id: the pickle_id if dag is pickled, None otherwise\n        :type pickle_id: int\n        :param start_date: the start date of the backfill job\n        :type start_date: datetime.datetime\n        :param session: the current session object\n        :type session: sqlalchemy.orm.session.Session\n        :return: the list of execution_dates for the finished dag runs\n        :rtype: list","confidence":0.73,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L2118-L2366","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L2118-L2366","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L2118-L2366","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L2118-L2366"},{"dataset":"csn-python","qid":"50","query":"Gets the DAG out of the dictionary, and refreshes it if expired","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L112-L143","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L112-L143","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L112-L143","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/dagbag.py#L112-L143"},{"dataset":"csn-python","qid":"505","query":"Assert `x` has rank equal to `rank` or smaller.\n\n  Example of adding a dependency to an operation:\n\n  ```python\n  with tf.control_dependencies([tf.assert_rank_at_most(x, 2)]):\n    output = tf.reduce_sum(x)\n  ```\n\n  Args:\n    x:  Numeric `Tensor`.\n    rank:  Scalar `Tensor`.\n    data:  The tensors to print out if the condition is False.  Defaults to\n      error message and first few entries of `x`.\n    summarize: Print this many entries of each tensor.\n    message: A string to prefix to the default message.\n    name: A name for this operation (optional).\n      Defaults to \"assert_rank_at_most\".\n\n  Returns:\n    Op raising `InvalidArgumentError` unless `x` has specified rank or lower.\n    If static checks determine `x` has correct rank, a `no_op` is returned.\n\n  Raises:\n    ValueError:  If static checks determine `x` has wrong rank.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/assert_util.py#L76-L106","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/assert_util.py#L76-L106","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/assert_util.py#L76-L106","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/assert_util.py#L76-L106"},{"dataset":"csn-python","qid":"506","query":"Decompresses data for Content-Encoding: gzip.","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L319-L326","reverseDid":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L319-L326","forwardChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L319-L326","reverseChoice":"https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/common.py#L319-L326"},{"dataset":"csn-python","qid":"508","query":"pre-request callback\n    params<dict>:\n      method  : POST/GET\n      headers : {}\n      data    : {} # if method == POST\n      url     : ''\n      category: 'search category'\n      pageno  : 1 # number of the requested page","confidence":0.45,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/asciimoo/searx/blob/a84caa22cf947e973c10aa968d35fb2bdda6d048/searx/engines/duden.py#L26-L39","reverseDid":"https://github.com/asciimoo/searx/blob/a84caa22cf947e973c10aa968d35fb2bdda6d048/searx/engines/duden.py#L26-L39","forwardChoice":"https://github.com/asciimoo/searx/blob/a84caa22cf947e973c10aa968d35fb2bdda6d048/searx/engines/duden.py#L26-L39","reverseChoice":"https://github.com/asciimoo/searx/blob/a84caa22cf947e973c10aa968d35fb2bdda6d048/searx/engines/duden.py#L26-L39"},{"dataset":"csn-python","qid":"510","query":"Converts (batch of) scalars to (batch of) positive valued scalars.\n\n  Args:\n    x: (Batch of) `float`-like `Tensor` representing scalars which will be\n      transformed into positive elements.\n    shift: `Tensor` added to `softplus` transformation of elements.\n      Default value: `1e-5`.\n    name: A `name_scope` name for operations created by this function.\n      Default value: `None` (i.e., \"positive_tril_with_shift\").\n\n  Returns:\n    scale: (Batch of) scalars`with `x.dtype` and `x.shape`.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/trainable_distributions/trainable_distributions_lib.py#L42-L61","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/trainable_distributions/trainable_distributions_lib.py#L42-L61","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/trainable_distributions/trainable_distributions_lib.py#L42-L61","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/trainable_distributions/trainable_distributions_lib.py#L42-L61"},{"dataset":"csn-python","qid":"52","query":"Execute the bash command in a temporary directory\n        which will be cleaned afterwards","confidence":0.85,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/operators/bash_operator.py#L86-L144","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/bash_sensor.py#L60-L91","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/operators/bash_operator.py#L86-L144","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/sensors/bash_sensor.py#L60-L91"},{"dataset":"csn-python","qid":"531","query":"Processes the log files and extracts useful information out of it.\n\n        If the deploy-mode is 'client', log the output of the submit command as those\n        are the output logs of the Spark worker directly.\n\n        Remark: If the driver needs to be tracked for its status, the log-level of the\n        spark deploy needs to be at least INFO (log4j.logger.org.apache.spark.deploy=INFO)\n\n        :param itr: An iterator which iterates over the input of the subprocess","confidence":0.79,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L378-L429","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L378-L429","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L378-L429","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_submit_hook.py#L378-L429"},{"dataset":"csn-python","qid":"532","query":"Classifies a document into categories.\n\n        :param document: Input document.\n            If a dict is provided, it must be of the same form as the protobuf message Document\n        :type document: dict or class google.cloud.language_v1.types.Document\n        :param retry: A retry object used to retry requests. If None is specified, requests will not be\n            retried.\n        :type retry: google.api_core.retry.Retry\n        :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if\n            retry is specified, the timeout applies to each individual attempt.\n        :type timeout: float\n        :param metadata: Additional metadata that is provided to the method.\n        :type metadata: sequence[tuple[str, str]]]\n        :rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse","confidence":0.75,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L198-L217","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L198-L217","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L198-L217","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L198-L217"},{"dataset":"csn-python","qid":"533","query":"Retrieves a database in Cloud Spanner. If the database does not exist\n        in the specified instance, it returns None.\n\n        :param instance_id: The ID of the Cloud Spanner instance.\n        :type instance_id: str\n        :param database_id: The ID of the database in Cloud Spanner.\n        :type database_id: str\n        :param project_id: Optional, the ID of the  GCP project that owns the Cloud Spanner\n            database. If set to None or missing, the default project_id from the GCP connection is used.\n        :type project_id: str\n        :return: Database object or None if database does not exist\n        :rtype: google.cloud.spanner_v1.database.Database or None","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L184-L209","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L184-L209","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L184-L209","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L184-L209"},{"dataset":"csn-python","qid":"537","query":"Compute log_prob(y) using log survival_function and cdf together.","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/quantized_distribution.py#L372-L394","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/quantized_distribution.py#L372-L394","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/quantized_distribution.py#L372-L394","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/quantized_distribution.py#L372-L394"},{"dataset":"csn-python","qid":"538","query":"Reconstruct input `x` from a its normalized version.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/normal.py#L234-L237","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/normal.py#L234-L237","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/normal.py#L234-L237","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/normal.py#L234-L237"},{"dataset":"csn-python","qid":"54","query":"A convenience method that provides all the features that analyzeSentiment,\n        analyzeEntities, and analyzeSyntax provide in one call.\n\n        :param document: Input document.\n            If a dict is provided, it must be of the same form as the protobuf message Document\n        :type document: dict or google.cloud.language_v1.types.Document\n        :param features: The enabled features.\n            If a dict is provided, it must be of the same form as the protobuf message Features\n        :type features: dict or google.cloud.language_v1.enums.Features\n        :param encoding_type: The encoding type used by the API to calculate offsets.\n        :type encoding_type: google.cloud.language_v1.types.EncodingType\n        :param retry: A retry object used to retry requests. If None is specified, requests will not be\n            retried.\n        :type retry: google.api_core.retry.Retry\n        :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if\n            retry is specified, the timeout applies to each individual attempt.\n        :type timeout: float\n        :param metadata: Additional metadata that is provided to the method.\n        :type metadata: sequence[tuple[str, str]]]\n        :rtype: google.cloud.language_v1.types.AnnotateTextResponse","confidence":0.83,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L163-L195","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L163-L195","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L163-L195","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L163-L195"},{"dataset":"csn-python","qid":"542","query":"Run migrations in 'offline' mode.\n\n    This configures the context with just a URL\n    and not an Engine, though an Engine is acceptable\n    here as well.  By skipping the Engine creation\n    we don't even need a DBAPI to be available.\n\n    Calls to context.execute() here emit the given string to the\n    script output.","confidence":0.8,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/migrations/env.py#L48-L65","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/migrations/env.py#L48-L65","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/migrations/env.py#L48-L65","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/migrations/env.py#L48-L65"},{"dataset":"csn-python","qid":"546","query":"add serie - Series are list of data that will be plotted\n        y {1, 2, 3, 4, 5} / x {1, 2, 3, 4, 5}\n\n        **Attributes**:\n\n            * ``name`` - set Serie name\n            * ``x`` - x-axis data\n            * ``y`` - y-axis data\n\n            kwargs:\n\n            * ``shape`` - for scatterChart, you can set different shapes\n                          (circle, triangle etc...)\n            * ``size`` - for scatterChart, you can set size of different shapes\n            * ``type`` - for multiChart, type should be bar\n            * ``bar`` - to display bars in Chart\n            * ``color_list`` - define list of colors which will be\n                               used by pieChart\n            * ``color`` - set axis color\n            * ``disabled`` -\n\n            extra:\n\n            * ``tooltip`` - set tooltip flag\n            * ``date_format`` - set date_format for tooltip if x-axis is in\n              date format","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L179-L294","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L179-L294","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L179-L294","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L179-L294"},{"dataset":"csn-python","qid":"547","query":"Get all the roles associated with the user.\n\n        :param user: the ab_user in FAB model.\n        :return: a list of roles associated with the user.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L221-L234","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L221-L234","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L221-L234","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L221-L234"},{"dataset":"csn-python","qid":"552","query":"Calls `fn`, appropriately reshaping its input `x` and output.","confidence":0.9,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/batch_reshape.py#L234-L262","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/batch_reshape.py#L234-L262","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/batch_reshape.py#L234-L262","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/batch_reshape.py#L234-L262"},{"dataset":"csn-python","qid":"563","query":"returns a json response from a json serializable python object","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/utils.py#L193-L201","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/utils.py#L193-L201","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/utils.py#L193-L201","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/utils.py#L193-L201"},{"dataset":"csn-python","qid":"567","query":"Static batch shape of models represented by this component.\n\n    Returns:\n      batch_shape: A `tf.TensorShape` giving the broadcast batch shape of\n        all model parameters. This should match the batch shape of\n        derived state space models, i.e.,\n        `self.make_state_space_model(...).batch_shape`. It may be partially\n        defined or unknown.","confidence":0.88,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L80-L94","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L80-L94","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L80-L94","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L80-L94"},{"dataset":"csn-python","qid":"57","query":"If the airflow scheduler restarts with pending \"Queued\" tasks, the tasks may or\n        may not\n        have been launched Thus, on starting up the scheduler let's check every\n        \"Queued\" task to\n        see if it has been launched (ie: if there is a corresponding pod on kubernetes)\n\n        If it has been launched then do nothing, otherwise reset the state to \"None\" so\n        the task\n        will be rescheduled\n\n        This will not be necessary in a future version of airflow in which there is\n        proper support\n        for State.LAUNCHED","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/executors/kubernetes_executor.py#L606-L653","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/executors/kubernetes_executor.py#L606-L653","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/executors/kubernetes_executor.py#L606-L653","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/executors/kubernetes_executor.py#L606-L653"},{"dataset":"csn-python","qid":"579","query":"Instantiates an initializer from a configuration dictionary.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/initializers.py#L119-L126","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/initializers.py#L119-L126","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/initializers.py#L119-L126","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/initializers.py#L119-L126"},{"dataset":"csn-python","qid":"58","query":"Launches a MLEngine job and wait for it to reach a terminal state.\n\n        :param project_id: The Google Cloud project id within which MLEngine\n            job will be launched.\n        :type project_id: str\n\n        :param job: MLEngine Job object that should be provided to the MLEngine\n            API, such as: ::\n\n                {\n                  'jobId': 'my_job_id',\n                  'trainingInput': {\n                    'scaleTier': 'STANDARD_1',\n                    ...\n                  }\n                }\n\n        :type job: dict\n\n        :param use_existing_job_fn: In case that a MLEngine job with the same\n            job_id already exist, this method (if provided) will decide whether\n            we should use this existing job, continue waiting for it to finish\n            and returning the job object. It should accepts a MLEngine job\n            object, and returns a boolean value indicating whether it is OK to\n            reuse the existing job. If 'use_existing_job_fn' is not provided,\n            we by default reuse the existing MLEngine job.\n        :type use_existing_job_fn: function\n\n        :return: The MLEngine job object if the job successfully reach a\n            terminal state (which might be FAILED or CANCELLED state).\n        :rtype: dict","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L60-L121","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L60-L121","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L60-L121","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L60-L121"},{"dataset":"csn-python","qid":"582","query":"Sample shape of random variable as a `TensorShape`.","confidence":0.83,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/random_variable.py#L133-L137","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/random_variable.py#L133-L137","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/random_variable.py#L133-L137","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/random_variable.py#L133-L137"},{"dataset":"csn-python","qid":"587","query":"Returns explict dtype from `args_list` if exists, else preferred_dtype.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/internal/utils.py#L58-L74","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/internal/utils.py#L58-L74","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/internal/utils.py#L58-L74","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/internal/utils.py#L58-L74"},{"dataset":"csn-python","qid":"59","query":"Get the current date and time in UTC\n    :return:","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/timezone.py#L52-L64","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/timezone.py#L52-L64","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/timezone.py#L52-L64","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/timezone.py#L52-L64"},{"dataset":"csn-python","qid":"597","query":"Runs Hook.run() with a Tenacity decorator attached to it. This is useful for\n        connectors which might be disturbed by intermittent issues and should not\n        instantly fail.\n\n        :param _retry_args: Arguments which define the retry behaviour.\n            See Tenacity documentation at https://github.com/jd/tenacity\n        :type _retry_args: dict\n\n\n        :Example::\n\n            hook = HttpHook(http_conn_id='my_conn',method='GET')\n            retry_args = dict(\n                 wait=tenacity.wait_exponential(),\n                 stop=tenacity.stop_after_attempt(10),\n                 retry=requests.exceptions.ConnectionError\n             )\n             hook.run_with_advanced_retry(\n                     endpoint='v1/test',\n                     _retry_args=retry_args\n                 )","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L183-L211","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L183-L211","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L183-L211","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/http_hook.py#L183-L211"},{"dataset":"csn-python","qid":"6","query":"Loads a tab-delimited file into a database table","confidence":0.88,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L374-L457","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L374-L457","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L374-L457","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/hive_hooks.py#L374-L457"},{"dataset":"csn-python","qid":"60","query":"Return a cosmos db client.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L50-L60","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L50-L60","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L50-L60","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L50-L60"},{"dataset":"csn-python","qid":"602","query":"Helper to broadcast `volatility_parts` to the shape of `state_parts`.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/langevin.py#L925-L929","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/langevin.py#L925-L929","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/langevin.py#L925-L929","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/langevin.py#L925-L929"},{"dataset":"csn-python","qid":"603","query":"Make a slug from the given text.\n    :param text (str): initial text\n    :param entities (bool):\n    :param decimal (bool):\n    :param hexadecimal (bool):\n    :param max_length (int): output string length\n    :param word_boundary (bool):\n    :param save_order (bool): if parameter is True and max_length > 0 return whole words in the initial order\n    :param separator (str): separator between words\n    :param stopwords (iterable): words to discount\n    :param regex_pattern (str): regex pattern for allowed characters\n    :param lowercase (bool): activate case sensitivity by setting it to False\n    :param replacements (iterable): list of replacement rules e.g. [['|', 'or'], ['%', 'percent']]\n    :return (str):","confidence":0.89,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/slugify/slugify.py#L74-L175","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/slugify/slugify.py#L74-L175","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/slugify/slugify.py#L74-L175","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/slugify/slugify.py#L74-L175"},{"dataset":"csn-python","qid":"607","query":"Takes a cursor, and writes the BigQuery schema in .json format for the\n        results to a local file system.\n\n        :return: A dictionary where key is a filename to be used as an object\n            name in GCS, and values are file handles to local files that\n            contains the BigQuery schema fields in .json format.","confidence":0.87,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/postgres_to_gcs_operator.py#L166-L192","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/postgres_to_gcs_operator.py#L166-L192","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/postgres_to_gcs_operator.py#L166-L192","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/postgres_to_gcs_operator.py#L166-L192"},{"dataset":"csn-python","qid":"614","query":"Helper which interpolates between two locs.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L853-L879","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L853-L879","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L853-L879","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L853-L879"},{"dataset":"csn-python","qid":"615","query":"Remote Popen (actually execute the Spark-sql query)\n\n        :param cmd: command to remotely execute\n        :param kwargs: extra arguments to Popen (see subprocess.Popen)","confidence":0.69,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_sql_hook.py#L136-L159","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_sql_hook.py#L136-L159","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_sql_hook.py#L136-L159","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/spark_sql_hook.py#L136-L159"},{"dataset":"csn-python","qid":"621","query":"Checks that a key matching a wildcard expression exists in a bucket\n\n        :param wildcard_key: the path to the key\n        :type wildcard_key: str\n        :param bucket_name: the name of the bucket\n        :type bucket_name: str\n        :param delimiter: the delimiter marks key hierarchy\n        :type delimiter: str","confidence":0.83,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L279-L293","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L279-L293","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L279-L293","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L279-L293"},{"dataset":"csn-python","qid":"622","query":"Returns a dictionary of {filename: {attributes}} for all files\n        on the remote system (where the MLSD command is supported).\n\n        :param path: full path to the remote directory\n        :type path: str","confidence":0.91,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ftp_hook.py#L102-L117","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ftp_hook.py#L102-L117","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ftp_hook.py#L102-L117","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ftp_hook.py#L102-L117"},{"dataset":"csn-python","qid":"623","query":"Save a PNG plot visualizing posterior uncertainty on heldout data.\n\n  Args:\n    input_vals: A `float`-like Numpy `array` of shape\n      `[num_heldout] + IMAGE_SHAPE`, containing heldout input images.\n    probs: A `float`-like Numpy array of shape `[num_monte_carlo,\n      num_heldout, num_classes]` containing Monte Carlo samples of\n      class probabilities for each heldout sample.\n    fname: Python `str` filename to save the plot to.\n    n: Python `int` number of datapoints to vizualize.\n    title: Python `str` title for the plot.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L124-L158","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L124-L158","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L124-L158","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L124-L158"},{"dataset":"csn-python","qid":"627","query":"Returns a shape based on `x` with at least the given `rank`.\n\n  For more details, see `help(tf.TensorShape.with_rank_at_least)`.\n\n  Args:\n    x: object representing a shape; convertible to `tf.TensorShape`.\n    rank: An `int` representing the minimum rank of `x` or else an assertion is\n      raised.\n\n  Returns:\n    shape: a shape having `type(x)` but guaranteed to have at least the given\n      rank (or else an assertion was raised).\n\n  Raises:\n    ValueError: If `x` does not represent a shape with at least the given\n      `rank`.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/tensorshape_util.py#L285-L303","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/tensorshape_util.py#L285-L303","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/tensorshape_util.py#L285-L303","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/tensorshape_util.py#L285-L303"},{"dataset":"csn-python","qid":"629","query":"Helper to broadcast a tensor using a list of target tensors.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/triangular.py#L33-L38","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/triangular.py#L33-L38","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/triangular.py#L33-L38","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/triangular.py#L33-L38"},{"dataset":"csn-python","qid":"632","query":"Auto correlation along one axis.\n\n  Given a `1-D` wide sense stationary (WSS) sequence `X`, the auto correlation\n  `RXX` may be defined as  (with `E` expectation and `Conj` complex conjugate)\n\n  ```\n  RXX[m] := E{ W[m] Conj(W[0]) } = E{ W[0] Conj(W[-m]) },\n  W[n]   := (X[n] - MU) / S,\n  MU     := E{ X[0] },\n  S**2   := E{ (X[0] - MU) Conj(X[0] - MU) }.\n  ```\n\n  This function takes the viewpoint that `x` is (along one axis) a finite\n  sub-sequence of a realization of (WSS) `X`, and then uses `x` to produce an\n  estimate of `RXX[m]` as follows:\n\n  After extending `x` from length `L` to `inf` by zero padding, the auto\n  correlation estimate `rxx[m]` is computed for `m = 0, 1, ..., max_lags` as\n\n  ```\n  rxx[m] := (L - m)**-1 sum_n w[n + m] Conj(w[n]),\n  w[n]   := (x[n] - mu) / s,\n  mu     := L**-1 sum_n x[n],\n  s**2   := L**-1 sum_n (x[n] - mu) Conj(x[n] - mu)\n  ```\n\n  The error in this estimate is proportional to `1 / sqrt(len(x) - m)`, so users\n  often set `max_lags` small enough so that the entire output is meaningful.\n\n  Note that since `mu` is an imperfect estimate of `E{ X[0] }`, and we divide by\n  `len(x) - m` rather than `len(x) - m - 1`, our estimate of auto correlation\n  contains a slight bias, which goes to zero as `len(x) - m --> infinity`.\n\n  Args:\n    x:  `float32` or `complex64` `Tensor`.\n    axis:  Python `int`. The axis number along which to compute correlation.\n      Other dimensions index different batch members.\n    max_lags:  Positive `int` tensor.  The maximum value of `m` to consider (in\n      equation above).  If `max_lags >= x.shape[axis]`, we effectively re-set\n      `max_lags` to `x.shape[axis] - 1`.\n    center:  Python `bool`.  If `False`, do not subtract the mean estimate `mu`\n      from `x[n]` when forming `w[n]`.\n    normalize:  Python `bool`.  If `False`, do not divide by the variance\n      estimate `s**2` when forming `w[n]`.\n    name:  `String` name to prepend to created ops.\n\n  Returns:\n    `rxx`: `Tensor` of same `dtype` as `x`.  `rxx.shape[i] = x.shape[i]` for\n      `i != axis`, and `rxx.shape[axis] = max_lags + 1`.\n\n  Raises:\n    TypeError:  If `x` is not a supported type.","confidence":0.69,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L39-L209","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L39-L209","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L39-L209","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L39-L209"},{"dataset":"csn-python","qid":"636","query":"generate HTML div","confidence":0.9,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L385-L405","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L385-L405","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L385-L405","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/_vendor/nvd3/NVD3Chart.py#L385-L405"},{"dataset":"csn-python","qid":"64","query":"Runs command and returns stdout","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L75-L93","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L75-L93","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L75-L93","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L75-L93"},{"dataset":"csn-python","qid":"641","query":"Build different type of Dingding message\n        As most commonly used type, text message just need post message content\n        rather than a dict like ``{'content': 'message'}``","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/dingding_hook.py#L76-L98","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/dingding_hook.py#L76-L98","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/dingding_hook.py#L76-L98","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/dingding_hook.py#L76-L98"},{"dataset":"csn-python","qid":"642","query":"Embeds checks that categorical distributions don't have too many classes.\n\n  A categorical-type distribution is one which, e.g., returns the class label\n  rather than a one-hot encoding.  E.g., `Categorical(probs)`.\n\n  Since distributions output samples in the same dtype as the parameters, we\n  must ensure that casting doesn't lose precision. That is, the\n  `parameter.dtype` implies a maximum number of classes. However, since shape is\n  `int32` and categorical variables are presumed to be indexes into a `Tensor`,\n  we must also ensure that the number of classes is no larger than the largest\n  possible `int32` index, i.e., `2**31-1`.\n\n  In other words the number of classes, `K`, must satisfy the following\n  condition:\n\n  ```python\n  K <= min(\n      int(2**31 - 1),  # Largest float as an index.\n      {\n          tf.float16: int(2**11),   # Largest int as a float16.\n          tf.float32: int(2**24),\n          tf.float64: int(2**53),\n      }.get(dtype_util.base_dtype(categorical_param.dtype), 0))\n  ```\n\n  Args:\n    categorical_param: Floating-point `Tensor` representing parameters of\n      distribution over categories. The rightmost shape is presumed to be the\n      number of categories.\n    name: A name for this operation (optional).\n\n  Returns:\n    categorical_param: Input `Tensor` with appropriate assertions embedded.\n\n  Raises:\n    TypeError: if `categorical_param` has an unknown `dtype`.\n    ValueError: if we can statically identify `categorical_param` as being too\n      large (for being closed under int32/float casting).","confidence":0.86,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L905-L999","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L905-L999","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L905-L999","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L905-L999"},{"dataset":"csn-python","qid":"648","query":"Save a PNG plot with histograms of weight means and stddevs.\n\n  Args:\n    names: A Python `iterable` of `str` variable names.\n    qm_vals: A Python `iterable`, the same length as `names`,\n      whose elements are Numpy `array`s, of any shape, containing\n      posterior means of weight varibles.\n    qs_vals: A Python `iterable`, the same length as `names`,\n      whose elements are Numpy `array`s, of any shape, containing\n      posterior standard deviations of weight varibles.\n    fname: Python `str` filename to save the plot to.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L90-L121","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L90-L121","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L90-L121","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/bayesian_neural_network.py#L90-L121"},{"dataset":"csn-python","qid":"649","query":"Returns `max` or `mask` if `max` is not finite.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/math.py#L172-L180","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/math.py#L172-L180","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/math.py#L172-L180","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/backend/numpy/math.py#L172-L180"},{"dataset":"csn-python","qid":"653","query":"Adjust brightness of an Image.\n\n    Args:\n        img (PIL Image): PIL Image to be adjusted.\n        brightness_factor (float):  How much to adjust the brightness. Can be\n            any non negative number. 0 gives a black image, 1 gives the\n            original image while 2 increases the brightness by a factor of 2.\n\n    Returns:\n        PIL Image: Brightness adjusted image.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L537-L554","reverseDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L537-L554","forwardChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L537-L554","reverseChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L537-L554"},{"dataset":"csn-python","qid":"657","query":"Instantiate this model as a Distribution over specified `num_timesteps`.\n\n    Args:\n      num_timesteps: Python `int` number of timesteps to model.\n      param_vals: a list of `Tensor` parameter values in order corresponding to\n        `self.parameters`, or a dict mapping from parameter names to values.\n      initial_state_prior: an optional `Distribution` instance overriding the\n        default prior on the model's initial state. This is used in forecasting\n        (\"today's prior is yesterday's posterior\").\n      initial_step: optional `int` specifying the initial timestep to model.\n        This is relevant when the model contains time-varying components,\n        e.g., holidays or seasonality.\n\n    Returns:\n      dist: a `LinearGaussianStateSpaceModel` Distribution object.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L134-L159","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L134-L159","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L134-L159","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/structural_time_series.py#L134-L159"},{"dataset":"csn-python","qid":"660","query":"Helper method to save images visualizing model reconstructions.\n\n  Args:\n    images_val: Numpy array containing a batch of input images.\n    reconstructed_images_val: Numpy array giving the expected output\n      (mean) of the decoder.\n    random_images_val: Optionally, a Numpy array giving the expected output\n      (mean) of decoding samples from the prior, or `None`.\n    log_dir: The directory to write images (Python `str`).\n    prefix: A specific label for the saved visualizations, which\n      determines their filenames (Python `str`).\n    viz_n: The number of images from each batch to visualize (Python `int`).","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vq_vae.py#L324-L350","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vq_vae.py#L324-L350","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vq_vae.py#L324-L350","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vq_vae.py#L324-L350"},{"dataset":"csn-python","qid":"661","query":"When using git to retrieve the DAGs, use the GitSync Init Container","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/kubernetes/worker_configuration.py#L45-L131","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/kubernetes/worker_configuration.py#L45-L131","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/kubernetes/worker_configuration.py#L45-L131","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/kubernetes/worker_configuration.py#L45-L131"},{"dataset":"csn-python","qid":"662","query":"Checks whether the operation list has an operation with the\n        expected status, then returns true\n        If it encounters operations in FAILED or ABORTED state\n        throw :class:`airflow.exceptions.AirflowException`.\n\n        :param operations: (Required) List of transfer operations to check.\n        :type operations: list[dict]\n        :param expected_statuses: (Required) status that is expected\n            See:\n            https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferOperations#Status\n        :type expected_statuses: set[str]\n        :return: If there is an operation with the expected state\n            in the operation list, returns true,\n        :raises: airflow.exceptions.AirflowException If it encounters operations\n            with a state in the list,\n        :rtype: bool","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L361-L397","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L361-L397","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L361-L397","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L361-L397"},{"dataset":"csn-python","qid":"663","query":"Maybe broadcasts `from_structure` to `to_structure`.\n\n  If `from_structure` is a singleton, it is tiled to match the structure of\n  `to_structure`. Note that the elements in `from_structure` are not copied if\n  this tiling occurs.\n\n  Args:\n    from_structure: A structure.\n    to_structure: A structure.\n\n  Returns:\n    new_from_structure: Same structure as `to_structure`.","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L160-L178","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L160-L178","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L160-L178","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L160-L178"},{"dataset":"csn-python","qid":"669","query":"Creates weighted `LinOp` from existing `LinOp`.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L899-L933","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L899-L933","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L899-L933","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/vector_diffeomixture.py#L899-L933"},{"dataset":"csn-python","qid":"670","query":"Computes I_v(z)*exp(-abs(z)) using a recurrence relation, where z > 0.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/von_mises_fisher.py#L36-L73","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/von_mises_fisher.py#L36-L73","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/von_mises_fisher.py#L36-L73","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/von_mises_fisher.py#L36-L73"},{"dataset":"csn-python","qid":"675","query":"Infers from the dates which dag runs need to be created and does so.\n    :param dag: the dag to create dag runs for\n    :param execution_dates: list of execution dates to evaluate\n    :param state: the state to set the dag run to\n    :param run_id_template:the template for run id to be with the execution date\n    :return: newly created and existing dag runs for the execution dates supplied","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L30-L53","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L30-L53","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L30-L53","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L30-L53"},{"dataset":"csn-python","qid":"68","query":"Upload a file to Azure Blob Storage.","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_wasb.py#L56-L64","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_wasb.py#L56-L64","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_wasb.py#L56-L64","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/file_to_wasb.py#L56-L64"},{"dataset":"csn-python","qid":"682","query":"Computes the standard deviation of a mixture distribution.\n\n  This function works regardless of the component distribution, so long as\n  each component's mean and standard deviation can be provided.\n\n  Args:\n    mixture_weight_vector: A 2D tensor with shape [batch_size, num_components]\n    mean_vector: A 2D tensor of mixture component means. Has shape `[batch_size,\n      num_components]`.\n    stddev_vector: A 2D tensor of mixture component standard deviations. Has\n      shape `[batch_size, num_components]`.\n\n  Returns:\n    A 1D tensor of shape `[batch_size]` representing the standard deviation of\n    the mixture distribution with given weights and component means and standard\n    deviations.\n  Raises:\n    ValueError: If the shapes of the input tensors are not as expected.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L39-L82","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L39-L82","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L39-L82","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L39-L82"},{"dataset":"csn-python","qid":"683","query":"Creates a new database in Cloud Spanner.\n\n        :type project_id: str\n        :param instance_id: The ID of the Cloud Spanner instance.\n        :type instance_id: str\n        :param database_id: The ID of the database to create in Cloud Spanner.\n        :type database_id: str\n        :param ddl_statements: The string list containing DDL for the new database.\n        :type ddl_statements: list[str]\n        :param project_id: Optional, the ID of the  GCP project that owns the Cloud Spanner\n            database. If set to None or missing, the default project_id from the GCP connection is used.\n        :return: None","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L212-L244","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L212-L244","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L212-L244","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_spanner_hook.py#L212-L244"},{"dataset":"csn-python","qid":"684","query":"Compute prior means for all variables via dynamic programming.\n\n    Returns:\n      latent_means: Prior means of latent states `z_t`, as a `Tensor`\n        of shape `batch_shape + [num_timesteps, latent_size]`\n      observation_means: Prior covariance matrices of observations\n        `x_t`, as a `Tensor` of shape `batch_shape + [num_timesteps,\n        observation_size]`","confidence":0.6,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L981-L1038","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L981-L1038","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L981-L1038","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L981-L1038"},{"dataset":"csn-python","qid":"689","query":"Determine the most appropriate time unit for an array of time durations\n    specified in seconds.\n    e.g. 5400 seconds => 'minutes', 36000 seconds => 'hours'","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dates.py#L195-L211","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dates.py#L195-L211","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dates.py#L195-L211","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/dates.py#L195-L211"},{"dataset":"csn-python","qid":"697","query":"Download the EMNIST data if it doesn't exist in processed_folder already.","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/datasets/mnist.py#L262-L304","reverseDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/datasets/mnist.py#L262-L304","forwardChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/datasets/mnist.py#L262-L304","reverseChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/datasets/mnist.py#L262-L304"},{"dataset":"csn-python","qid":"703","query":"Invoke Lambda Function","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_lambda_hook.py#L53-L68","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_lambda_hook.py#L53-L68","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_lambda_hook.py#L53-L68","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_lambda_hook.py#L53-L68"},{"dataset":"csn-python","qid":"704","query":"Replaces an element at supplied index.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/nelder_mead.py#L602-L606","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/nelder_mead.py#L602-L606","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/nelder_mead.py#L602-L606","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/nelder_mead.py#L602-L606"},{"dataset":"csn-python","qid":"707","query":"Runs one iteration of the Transformed Kernel.\n\n    Args:\n      current_state: `Tensor` or Python `list` of `Tensor`s\n        representing the current state(s) of the Markov chain(s),\n        _after_ application of `bijector.forward`. The first `r`\n        dimensions index independent chains,\n        `r = tf.rank(target_log_prob_fn(*current_state))`. The\n        `inner_kernel.one_step` does not actually use `current_state`,\n        rather it takes as input\n        `previous_kernel_results.transformed_state` (because\n        `TransformedTransitionKernel` creates a copy of the input\n        inner_kernel with a modified `target_log_prob_fn` which\n        internally applies the `bijector.forward`).\n      previous_kernel_results: `collections.namedtuple` containing `Tensor`s\n        representing values from previous calls to this function (or from the\n        `bootstrap_results` function.)\n\n    Returns:\n      next_state: Tensor or Python list of `Tensor`s representing the state(s)\n        of the Markov chain(s) after taking exactly one step. Has same type and\n        shape as `current_state`.\n      kernel_results: `collections.namedtuple` of internal calculations used to\n        advance the chain.","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273"},{"dataset":"csn-python","qid":"711","query":"Brackets the minimum given an initial starting point.\n\n  Applies the Hager Zhang bracketing algorithm to find an interval containing\n  a region with points satisfying Wolfe conditions. Uses the supplied initial\n  step size 'c', the right end point of the provided search interval, to find\n  such an interval. The only condition on 'c' is that it should be positive.\n  For more details see steps B0-B3 in [Hager and Zhang (2006)][2].\n\n  Args:\n    value_and_gradients_function: A Python callable that accepts a real scalar\n      tensor and returns a namedtuple containing the value filed `f` of the\n      function and its derivative value field `df` at that point.\n      Alternatively, the function may representthe batching of `n` such line\n      functions (e.g. projecting a single multivariate objective function along\n      `n` distinct directions at once) accepting n points as input, i.e. a\n      tensor of shape [n], and return a tuple of two tensors of shape [n], the\n      function values and the corresponding derivatives at the input points.\n    search_interval: A namedtuple describing the current search interval,\n      must include the fields:\n      - converged: Boolean `Tensor` of shape [n], indicating batch members\n          where search has already converged. Interval for these batch members\n          wont be modified.\n      - failed: Boolean `Tensor` of shape [n], indicating batch members\n          where search has already failed. Interval for these batch members\n          wont be modified.\n      - iterations: Scalar int32 `Tensor`. Number of line search iterations\n          so far.\n      - func_evals: Scalar int32 `Tensor`. Number of function evaluations\n          so far.\n      - left: A namedtuple, as returned by value_and_gradients_function\n          evaluated at 0, the left end point of the current interval.\n      - right: A namedtuple, as returned by value_and_gradients_function,\n          of the right end point of the current interval (labelled 'c' above).\n    f_lim: real `Tensor` of shape [n]. The function value threshold for\n      the approximate Wolfe conditions to be checked for each batch member.\n    max_iterations: Int32 scalar `Tensor`. The maximum number of iterations\n      permitted. The limit applies equally to all batch members.\n    expansion_param: Scalar positive `Tensor` of real dtype. Must be greater\n      than `1.`. Used to expand the initial interval in case it does not bracket\n      a minimum.\n\n  Returns:\n    A namedtuple with the following fields.\n      iteration: An int32 scalar `Tensor`. The number of iterations performed.\n        Bounded above by `max_iterations` parameter.\n      stopped: A boolean `Tensor` of shape [n]. True for those batch members\n        where the algorithm terminated before reaching `max_iterations`.\n      failed: A boolean `Tensor` of shape [n]. True for those batch members\n        where an error was encountered during bracketing.\n      num_evals: An int32 scalar `Tensor`. The number of times the objective\n        function was evaluated.\n      left: Return value of value_and_gradients_function at the updated left\n        end point of the interval found.\n      right: Return value of value_and_gradients_function at the updated right\n        end point of the interval found.","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L426-L545","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L426-L545","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L426-L545","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L426-L545"},{"dataset":"csn-python","qid":"714","query":"Yields the top-most interceptor on the thread-local interceptor stack.\n\n  Operations may be intercepted by multiple nested interceptors. Once reached,\n  an operation can be forwarded through nested interceptors until resolved.\n  To allow for nesting, implement interceptors by re-wrapping their first\n  argument (`f`) as an `interceptable`. To avoid nesting, manipulate the\n  computation without using `interceptable`.\n\n  This function allows for nesting by manipulating the thread-local interceptor\n  stack, so that operations are intercepted in the order of interceptor nesting.\n\n  #### Examples\n\n  ```python\n  from tensorflow_probability import edward2 as ed\n\n  def model():\n    x = ed.Normal(loc=0., scale=1., name=\"x\")\n    y = ed.Normal(loc=x, scale=1., name=\"y\")\n    return x + y\n\n  def double(f, *args, **kwargs):\n    return 2. * interceptable(f)(*args, **kwargs)\n\n  def set_y(f, *args, **kwargs):\n    if kwargs.get(\"name\") == \"y\":\n      kwargs[\"value\"] = 0.42\n    return interceptable(f)(*args, **kwargs)\n\n  with interception(double):\n    with interception(set_y):\n      z = model()\n  ```\n\n  This will firstly put `double` on the stack, and then `set_y`,\n  resulting in the stack:\n  (TOP) set_y -> double -> apply (BOTTOM)\n\n  The execution of `model` is then (top lines are current stack state):\n  1) (TOP) set_y -> double -> apply (BOTTOM);\n  `ed.Normal(0., 1., \"x\")` is intercepted by `set_y`, and as the name is not \"y\"\n  the operation is simply forwarded to the next interceptor on the stack.\n\n  2) (TOP) double -> apply (BOTTOM);\n  `ed.Normal(0., 1., \"x\")` is intercepted by `double`, to produce\n  `2*ed.Normal(0., 1., \"x\")`, with the operation being forwarded down the stack.\n\n  3) (TOP) apply (BOTTOM);\n  `ed.Normal(0., 1., \"x\")` is intercepted by `apply`, which simply calls the\n  constructor.\n\n  (At this point, the nested calls to `get_next_interceptor()`, produced by\n  forwarding operations, exit, and the current stack is again:\n  (TOP) set_y -> double -> apply (BOTTOM))\n\n  4) (TOP) set_y -> double -> apply (BOTTOM);\n  `ed.Normal(0., 1., \"y\")` is intercepted by `set_y`,\n  the value of `y` is set to 0.42 and the operation is forwarded down the stack.\n\n  5) (TOP) double -> apply (BOTTOM);\n  `ed.Normal(0., 1., \"y\")` is intercepted by `double`, to produce\n  `2*ed.Normal(0., 1., \"y\")`, with the operation being forwarded down the stack.\n\n  6) (TOP) apply (BOTTOM);\n  `ed.Normal(0., 1., \"y\")` is intercepted by `apply`, which simply calls the\n  constructor.\n\n  The final values for `x` and `y` inside of `model()` are tensors where `x` is\n  a random draw from Normal(0., 1.) doubled, and `y` is a constant 0.84, thus\n  z = 2 * Normal(0., 1.) + 0.84.","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/interceptor.py#L96-L172","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/interceptor.py#L96-L172","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/interceptor.py#L96-L172","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/interceptor.py#L96-L172"},{"dataset":"csn-python","qid":"718","query":"Propagate covariance through linear Gaussian transformation.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1729-L1732","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1729-L1732","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1729-L1732","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1729-L1732"},{"dataset":"csn-python","qid":"721","query":"Check if bucket_name exists.\n\n        :param bucket_name: the name of the bucket\n        :type bucket_name: str","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L48-L60","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L48-L60","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L48-L60","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/S3_hook.py#L48-L60"},{"dataset":"csn-python","qid":"724","query":"Method to stream data into BigQuery one record at a time without needing\n        to run a load job\n\n        .. seealso::\n            For more information, see:\n            https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/insertAll\n\n        :param project_id: The name of the project where we have the table\n        :type project_id: str\n        :param dataset_id: The name of the dataset where we have the table\n        :type dataset_id: str\n        :param table_id: The name of the table\n        :type table_id: str\n        :param rows: the rows to insert\n        :type rows: list\n\n        **Example or rows**:\n            rows=[{\"json\": {\"a_key\": \"a_value_0\"}}, {\"json\": {\"a_key\": \"a_value_1\"}}]\n\n        :param ignore_unknown_values: [Optional] Accept rows that contain values\n            that do not match the schema. The unknown values are ignored.\n            The default value  is false, which treats unknown values as errors.\n        :type ignore_unknown_values: bool\n        :param skip_invalid_rows: [Optional] Insert all valid rows of a request,\n            even if invalid rows exist. The default value is false, which causes\n            the entire request to fail if any invalid rows exist.\n        :type skip_invalid_rows: bool\n        :param fail_on_error: [Optional] Force the task to fail if any errors occur.\n            The default value is false, which indicates the task should not fail\n            even if any insertion errors occur.\n        :type fail_on_error: bool","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1705-L1779","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1705-L1779","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1705-L1779","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/bigquery_hook.py#L1705-L1779"},{"dataset":"csn-python","qid":"726","query":"Finish computation of log_prob on one element of the inverse image.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/transformed_distribution.py#L415-L430","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/transformed_distribution.py#L415-L430","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/transformed_distribution.py#L415-L430","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/transformed_distribution.py#L415-L430"},{"dataset":"csn-python","qid":"728","query":"Creates the Version on Google Cloud ML Engine.\n\n        Returns the operation if the version was created successfully and\n        raises an error otherwise.","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L165-L183","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L165-L183","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L165-L183","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L165-L183"},{"dataset":"csn-python","qid":"735","query":"Computes the output shape of the layer.\n\n    Args:\n      input_shape: Shape tuple (tuple of integers) or list of shape tuples\n        (one per output tensor of the layer). Shape tuples can include None for\n        free dimensions, instead of an integer.\n\n    Returns:\n      output_shape: A tuple representing the output shape.\n\n    Raises:\n      ValueError: If innermost dimension of `input_shape` is not defined.","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/dense_variational.py#L193-L213","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/dense_variational.py#L193-L213","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/dense_variational.py#L193-L213","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/dense_variational.py#L193-L213"},{"dataset":"csn-python","qid":"744","query":"Construct predictive distribution over future observations.\n\n  Given samples from the posterior over parameters, return the predictive\n  distribution over future observations for num_steps_forecast timesteps.\n\n  Args:\n    model: An instance of `StructuralTimeSeries` representing a\n      time-series model. This represents a joint distribution over\n      time-series and their parameters with batch shape `[b1, ..., bN]`.\n    observed_time_series: `float` `Tensor` of shape\n      `concat([sample_shape, model.batch_shape, [num_timesteps, 1]])` where\n      `sample_shape` corresponds to i.i.d. observations, and the trailing `[1]`\n      dimension may (optionally) be omitted if `num_timesteps > 1`. May\n      optionally be an instance of `tfp.sts.MaskedTimeSeries` including a\n      mask `Tensor` to encode the locations of missing observations.\n    parameter_samples: Python `list` of `Tensors` representing posterior samples\n      of model parameters, with shapes `[concat([[num_posterior_draws],\n      param.prior.batch_shape, param.prior.event_shape]) for param in\n      model.parameters]`. This may optionally also be a map (Python `dict`) of\n      parameter names to `Tensor` values.\n    num_steps_forecast: scalar `int` `Tensor` number of steps to forecast.\n\n  Returns:\n    forecast_dist: a `tfd.MixtureSameFamily` instance with event shape\n      [num_steps_forecast, 1] and batch shape\n      `concat([sample_shape, model.batch_shape])`, with `num_posterior_draws`\n      mixture components.\n\n  #### Examples\n\n  Suppose we've built a model and fit it to data using HMC:\n\n  ```python\n    day_of_week = tfp.sts.Seasonal(\n        num_seasons=7,\n        observed_time_series=observed_time_series,\n        name='day_of_week')\n    local_linear_trend = tfp.sts.LocalLinearTrend(\n        observed_time_series=observed_time_series,\n        name='local_linear_trend')\n    model = tfp.sts.Sum(components=[day_of_week, local_linear_trend],\n                        observed_time_series=observed_time_series)\n\n    samples, kernel_results = tfp.sts.fit_with_hmc(model, observed_time_series)\n  ```\n\n  Passing the posterior samples into `forecast`, we construct a forecast\n  distribution:\n\n  ```python\n    forecast_dist = tfp.sts.forecast(model, observed_time_series,\n                                     parameter_samples=samples,\n                                     num_steps_forecast=50)\n\n    forecast_mean = forecast_dist.mean()[..., 0]  # shape: [50]\n    forecast_scale = forecast_dist.stddev()[..., 0]  # shape: [50]\n    forecast_samples = forecast_dist.sample(10)[..., 0]  # shape: [10, 50]\n  ```\n\n  If using variational inference instead of HMC, we'd construct a forecast using\n  samples from the variational posterior:\n\n  ```python\n    (variational_loss,\n     variational_distributions) = tfp.sts.build_factored_variational_loss(\n       model=model, observed_time_series=observed_time_series)\n\n    # OMITTED: take steps to optimize variational loss\n\n    samples = {k: q.sample(30) for (k, q) in variational_distributions.items()}\n    forecast_dist = tfp.sts.forecast(model, observed_time_series,\n                                         parameter_samples=samples,\n                                         num_steps_forecast=50)\n  ```\n\n  We can visualize the forecast by plotting:\n\n  ```python\n    from matplotlib import pylab as plt\n    def plot_forecast(observed_time_series,\n                      forecast_mean,\n                      forecast_scale,\n                      forecast_samples):\n      plt.figure(figsize=(12, 6))\n\n      num_steps = observed_time_series.shape[-1]\n      num_steps_forecast = forecast_mean.shape[-1]\n      num_steps_train = num_steps - num_steps_forecast\n\n      c1, c2 = (0.12, 0.47, 0.71), (1.0, 0.5, 0.05)\n      plt.plot(np.arange(num_steps), observed_time_series,\n               lw=2, color=c1, label='ground truth')\n\n      forecast_steps = np.arange(num_steps_train,\n                       num_steps_train+num_steps_forecast)\n      plt.plot(forecast_steps, forecast_samples.T, lw=1, color=c2, alpha=0.1)\n      plt.plot(forecast_steps, forecast_mean, lw=2, ls='--', color=c2,\n               label='forecast')\n      plt.fill_between(forecast_steps,\n                       forecast_mean - 2 * forecast_scale,\n                       forecast_mean + 2 * forecast_scale, color=c2, alpha=0.2)\n\n      plt.xlim([0, num_steps])\n      plt.legend()\n\n    plot_forecast(observed_time_series,\n                  forecast_mean=forecast_mean,\n                  forecast_scale=forecast_scale,\n                  forecast_samples=forecast_samples)\n  ```","confidence":0.92,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/forecast.py#L172-L362","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/forecast.py#L172-L362","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/forecast.py#L172-L362","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/forecast.py#L172-L362"},{"dataset":"csn-python","qid":"747","query":"Add or subtract days from a YYYY-MM-DD\n\n    :param ds: anchor date in ``YYYY-MM-DD`` format to add to\n    :type ds: str\n    :param days: number of days to add to the ds, you can use negative values\n    :type days: int\n\n    >>> ds_add('2015-01-01', 5)\n    '2015-01-06'\n    >>> ds_add('2015-01-06', -5)\n    '2015-01-01'","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/__init__.py#L28-L46","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/__init__.py#L28-L46","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/__init__.py#L28-L46","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/macros/__init__.py#L28-L46"},{"dataset":"csn-python","qid":"749","query":"Push latent means and covariances forward through the observation model.\n\n    Args:\n      latent_means: float `Tensor` of shape `[..., num_timesteps, latent_size]`\n      latent_covs: float `Tensor` of shape\n        `[..., num_timesteps, latent_size, latent_size]`.\n\n    Returns:\n      observation_means: float `Tensor` of shape\n        `[..., num_timesteps, observation_size]`\n      observation_covs: float `Tensor` of shape\n        `[..., num_timesteps, observation_size, observation_size]`","confidence":0.87,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1097-L1145","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1097-L1145","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1097-L1145","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1097-L1145"},{"dataset":"csn-python","qid":"75","query":"Recognizes audio input\n\n        :param config: information to the recognizer that specifies how to process the request.\n            https://googleapis.github.io/google-cloud-python/latest/speech/gapic/v1/types.html#google.cloud.speech_v1.types.RecognitionConfig\n        :type config: dict or google.cloud.speech_v1.types.RecognitionConfig\n        :param audio: audio data to be recognized\n            https://googleapis.github.io/google-cloud-python/latest/speech/gapic/v1/types.html#google.cloud.speech_v1.types.RecognitionAudio\n        :type audio: dict or google.cloud.speech_v1.types.RecognitionAudio\n        :param retry: (Optional) A retry object used to retry requests. If None is specified,\n            requests will not be retried.\n        :type retry: google.api_core.retry.Retry\n        :param timeout: (Optional) The amount of time, in seconds, to wait for the request to complete.\n            Note that if retry is specified, the timeout applies to each individual attempt.\n        :type timeout: float","confidence":0.92,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_speech_to_text_hook.py#L53-L73","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_speech_to_text_hook.py#L53-L73","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_speech_to_text_hook.py#L53-L73","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_speech_to_text_hook.py#L53-L73"},{"dataset":"csn-python","qid":"753","query":"field_name from kernel_results or kernel_results.accepted_results.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/replica_exchange_mc.py#L569-L575","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/replica_exchange_mc.py#L569-L575","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/replica_exchange_mc.py#L569-L575","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/replica_exchange_mc.py#L569-L575"},{"dataset":"csn-python","qid":"760","query":"Decorator function for argument bounds checking.\n\n  This decorator is meant to be used with methods that require the first\n  argument to be in the support of the distribution. If `validate_args` is\n  `True`, the method is wrapped with an assertion that the first argument is\n  greater than or equal to `loc`, since the support of the half-Cauchy\n  distribution is given by `[loc, infinity)`.\n\n\n  Args:\n    f: method to be decorated.\n\n  Returns:\n    Returns a decorated method that, when `validate_args` attribute of the class\n    is `True`, will assert that all elements in the first argument are within\n    the support of the distribution before executing the original method.","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/half_cauchy.py#L36-L63","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/half_cauchy.py#L36-L63","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/half_cauchy.py#L36-L63","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/half_cauchy.py#L36-L63"},{"dataset":"csn-python","qid":"761","query":"The Modified-GAN Csiszar-function in log-space.\n\n  A Csiszar-function is a member of,\n\n  ```none\n  F = { f:R_+ to R : f convex }.\n  ```\n\n  When `self_normalized = True` the modified-GAN (Generative/Adversarial\n  Network) Csiszar-function is:\n\n  ```none\n  f(u) = log(1 + u) - log(u) + 0.5 (u - 1)\n  ```\n\n  When `self_normalized = False` the `0.5 (u - 1)` is omitted.\n\n  The unmodified GAN Csiszar-function is identical to Jensen-Shannon (with\n  `self_normalized = False`).\n\n  Warning: this function makes non-log-space calculations and may therefore be\n  numerically unstable for `|logu| >> 0`.\n\n  Args:\n    logu: `float`-like `Tensor` representing `log(u)` from above.\n    self_normalized: Python `bool` indicating whether `f'(u=1)=0`. When\n      `f'(u=1)=0` the implied Csiszar f-Divergence remains non-negative even\n      when `p, q` are unnormalized measures.\n    name: Python `str` name prefixed to Ops created by this function.\n\n  Returns:\n    chi_square_of_u: `float`-like `Tensor` of the Csiszar-function evaluated\n      at `u = exp(logu)`.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/vi/csiszar_divergence.py#L620-L661","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/vi/csiszar_divergence.py#L620-L661","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/vi/csiszar_divergence.py#L620-L661","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/vi/csiszar_divergence.py#L620-L661"},{"dataset":"csn-python","qid":"769","query":"Filters inputs to be compatible with function `f`'s signature.\n\n  Args:\n    f: Function according to whose input signature we filter arguments.\n    src_kwargs: Keyword arguments to filter according to `f`.\n\n  Returns:\n    kwargs: Dict of key-value pairs in `src_kwargs` which exist in `f`'s\n      signature.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/program_transformations.py#L226-L246","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/program_transformations.py#L226-L246","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/program_transformations.py#L226-L246","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/program_transformations.py#L226-L246"},{"dataset":"csn-python","qid":"771","query":"Estimate standard deviation using samples.\n\n  Given `N` samples of scalar valued random variable `X`, standard deviation may\n  be estimated as\n\n  ```none\n  Stddev[X] := Sqrt[Var[X]],\n  Var[X] := N^{-1} sum_{n=1}^N (X_n - Xbar) Conj{(X_n - Xbar)},\n  Xbar := N^{-1} sum_{n=1}^N X_n\n  ```\n\n  ```python\n  x = tf.random_normal(shape=(100, 2, 3))\n\n  # stddev[i, j] is the sample standard deviation of the (i, j) batch member.\n  stddev = tfp.stats.stddev(x, sample_axis=0)\n  ```\n\n  Scaling a unit normal by a standard deviation produces normal samples\n  with that standard deviation.\n\n  ```python\n  observed_data = read_data_samples(...)\n  stddev = tfp.stats.stddev(observed_data)\n\n  # Make fake_data with the same standard deviation as observed_data.\n  fake_data = stddev * tf.random_normal(shape=(100,))\n  ```\n\n  Notice we divide by `N` (the numpy default), which does not create `NaN`\n  when `N = 1`, but is slightly biased.\n\n  Args:\n    x:  A numeric `Tensor` holding samples.\n    sample_axis: Scalar or vector `Tensor` designating axis holding samples, or\n      `None` (meaning all axis hold samples).\n      Default value: `0` (leftmost dimension).\n    keepdims:  Boolean.  Whether to keep the sample axis as singletons.\n    name: Python `str` name prefixed to Ops created by this function.\n          Default value: `None` (i.e., `'stddev'`).\n\n  Returns:\n    stddev: A `Tensor` of same `dtype` as the `x`, and rank equal to\n      `rank(x) - len(sample_axis)`","confidence":0.8,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L552-L599","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L552-L599","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L552-L599","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/sample_stats.py#L552-L599"},{"dataset":"csn-python","qid":"773","query":"Factory function to make random variable given distribution class.","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/generated_random_variables.py#L148-L175","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/generated_random_variables.py#L148-L175","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/generated_random_variables.py#L148-L175","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/edward2/generated_random_variables.py#L148-L175"},{"dataset":"csn-python","qid":"775","query":"Get static number of dimensions and assert that some expectations are met.\n\n  This function returns the number of dimensions 'ndims' of x, as a Python int.\n\n  The optional expect arguments are used to check the ndims of x, but this is\n  only done if the static ndims of x is not None.\n\n  Args:\n    x:  A Tensor.\n    expect_static:  Expect `x` to have statically defined `ndims`.\n    expect_ndims:  Optional Python integer.  If provided, assert that x has\n      number of dimensions equal to this.\n    expect_ndims_no_more_than:  Optional Python integer.  If provided, assert\n      that x has no more than this many dimensions.\n    expect_ndims_at_least:  Optional Python integer.  If provided, assert that x\n      has at least this many dimensions.\n\n  Returns:\n    ndims:  A Python integer.\n\n  Raises:\n    ValueError:  If any of the expectations above are violated.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/quantiles.py#L726-L787","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/quantiles.py#L726-L787","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/quantiles.py#L726-L787","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/stats/quantiles.py#L726-L787"},{"dataset":"csn-python","qid":"792","query":"Returns an image tensor.","confidence":0.23,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/sprites_dataset.py#L113-L118","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/sprites_dataset.py#L113-L118","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/sprites_dataset.py#L113-L118","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/sprites_dataset.py#L113-L118"},{"dataset":"csn-python","qid":"793","query":"Return `Tensor` with right-most ndims summed.\n\n  Args:\n    x: the `Tensor` whose right-most `ndims` dimensions to sum\n    ndims: number of right-most dimensions to sum.\n\n  Returns:\n    A `Tensor` resulting from calling `reduce_sum` on the `ndims` right-most\n    dimensions. If the shape of `x` is statically known, the result will also\n    have statically known shape. Otherwise, the resulting shape will only be\n    known at runtime.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/positive_semidefinite_kernels/internal/util.py#L68-L86","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/positive_semidefinite_kernels/internal/util.py#L68-L86","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/positive_semidefinite_kernels/internal/util.py#L68-L86","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/positive_semidefinite_kernels/internal/util.py#L68-L86"},{"dataset":"csn-python","qid":"796","query":"Return a set of dags that user has access to(either read or write).\n\n        :param username: Name of the user.\n        :return: A set of dag ids that the user could access.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L246-L266","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L246-L266","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L246-L266","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/security.py#L246-L266"},{"dataset":"csn-python","qid":"797","query":"Computes the doubling increments for the left end point.\n\n  The doubling procedure expands an initial interval to find a superset of the\n  true slice. At each doubling iteration, the interval width is doubled to\n  either the left or the right hand side with equal probability.\n  If, initially, the left end point is at `L(0)` and the width of the\n  interval is `w(0)`, then the left end point and the width at the\n  k-th iteration (denoted L(k) and w(k) respectively) are given by the following\n  recursions:\n\n  ```none\n  w(k) = 2 * w(k-1)\n  L(k) = L(k-1) - w(k-1) * X_k, X_k ~ Bernoulli(0.5)\n  or, L(0) - L(k) = w(0) Sum(2^i * X(i+1), 0 <= i < k)\n  ```\n\n  This function computes the sequence of `L(0)-L(k)` and `w(k)` for k between 0\n  and `max_doublings` independently for each chain.\n\n  Args:\n    batch_shape: Positive int32 `tf.Tensor`. The batch shape.\n    max_doublings: Scalar positive int32 `tf.Tensor`. The maximum number of\n      doublings to consider.\n    step_size: A real `tf.Tensor` with shape compatible with [num_chains].\n      The size of the initial interval.\n    seed: (Optional) positive int. The random seed. If None, no seed is set.\n    name: Python `str` name prefixed to Ops created by this function.\n      Default value: `None` (i.e., 'find_slice_bounds').\n\n  Returns:\n    left_increments: A tensor of shape (max_doublings+1, batch_shape). The\n      relative position of the left end point after the doublings.\n    widths: A tensor of shape (max_doublings+1, ones_like(batch_shape)). The\n      widths of the intervals at each stage of the doubling.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/slice_sampler_utils.py#L26-L87","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/slice_sampler_utils.py#L26-L87","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/slice_sampler_utils.py#L26-L87","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/slice_sampler_utils.py#L26-L87"},{"dataset":"csn-python","qid":"798","query":"Launch a process to process the given file.\n\n        :param result_queue: the queue to use for passing back the result\n        :type result_queue: multiprocessing.Queue\n        :param file_path: the file to process\n        :type file_path: unicode\n        :param pickle_dags: whether to pickle the DAGs found in the file and\n            save them to the DB\n        :type pickle_dags: bool\n        :param dag_id_white_list: if specified, only examine DAG ID's that are\n            in this list\n        :type dag_id_white_list: list[unicode]\n        :param thread_name: the name to use for the process that is launched\n        :type thread_name: unicode\n        :return: the process that was launched\n        :rtype: multiprocessing.Process\n        :param zombies: zombie task instances to kill\n        :type zombies: list[airflow.utils.dag_processing.SimpleTaskInstance]","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L342-L417","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L342-L417","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L342-L417","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L342-L417"},{"dataset":"csn-python","qid":"799","query":"Like tf.case, except attempts to statically evaluate predicates.\n\n  If any predicate in `pred_fn_pairs` is a bool or has a constant value, the\n  associated callable will be called or omitted depending on its value.\n  Otherwise this functions like tf.case.\n\n  Args:\n    pred_fn_pairs: Dict or list of pairs of a boolean scalar tensor and a\n                   callable which returns a list of tensors.\n    default: Optional callable that returns a list of tensors.\n    exclusive: True iff at most one predicate is allowed to evaluate to `True`.\n    name: A name for this operation (optional).\n\n  Returns:\n    The tensors returned by the first pair whose predicate evaluated to True, or\n    those returned by `default` if none does.\n\n  Raises:\n    TypeError: If `pred_fn_pairs` is not a list/dictionary.\n    TypeError: If `pred_fn_pairs` is a list but does not contain 2-tuples.\n    TypeError: If `fns[i]` is not callable for any i, or `default` is not\n               callable.","confidence":0.32,"flipped":true,"forwardCorrect":false,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/prefer_static.py#L120-L151","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/prefer_static.py#L154-L179","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/prefer_static.py#L120-L151","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/prefer_static.py#L154-L179"},{"dataset":"csn-python","qid":"810","query":"r\"\"\"Fits a GLM using coordinate-wise FIM-informed proximal gradient descent.\n\n  This function uses a L1- and L2-regularized, second-order quasi-Newton method\n  to find maximum-likelihood parameters for the given model and observed data.\n  The second-order approximations use negative Fisher information in place of\n  the Hessian, that is,\n\n  ```none\n  FisherInfo = E_Y[Hessian with respect to model_coefficients of -LogLikelihood(\n      Y | model_matrix, current value of model_coefficients)]\n  ```\n\n  For large, sparse data sets, `model_matrix` should be supplied as a\n  `SparseTensor`.\n\n  Args:\n    model_matrix: (Batch of) matrix-shaped, `float` `Tensor` or `SparseTensor`\n      where each row represents a sample's features.  Has shape `[N, n]` where\n      `N` is the number of data samples and `n` is the number of features per\n      sample.\n    response: (Batch of) vector-shaped `Tensor` with the same dtype as\n      `model_matrix` where each element represents a sample's observed response\n      (to the corresponding row of features).\n    model: `tfp.glm.ExponentialFamily`-like instance, which specifies the link\n      function and distribution of the GLM, and thus characterizes the negative\n      log-likelihood which will be minimized. Must have sufficient statistic\n      equal to the response, that is, `T(y) = y`.\n    model_coefficients_start: (Batch of) vector-shaped, `float` `Tensor` with\n      the same dtype as `model_matrix`, representing the initial values of the\n      coefficients for the GLM regression.  Has shape `[n]` where `model_matrix`\n      has shape `[N, n]`.\n    tolerance: scalar, `float` `Tensor` representing the tolerance for each\n      optiization step; see the `tolerance` argument of `fit_sparse_one_step`.\n    l1_regularizer: scalar, `float` `Tensor` representing the weight of the L1\n      regularization term.\n    l2_regularizer: scalar, `float` `Tensor` representing the weight of the L2\n      regularization term.\n      Default value: `None` (i.e., no L2 regularization).\n    maximum_iterations: Python integer specifying maximum number of iterations\n      of the outer loop of the optimizer (i.e., maximum number of calls to\n      `fit_sparse_one_step`).  After this many iterations of the outer loop, the\n      algorithm will terminate even if the return value `model_coefficients` has\n      not converged.\n      Default value: `1`.\n    maximum_full_sweeps_per_iteration: Python integer specifying the maximum\n      number of coordinate descent sweeps allowed in each iteration.\n      Default value: `1`.\n    learning_rate: scalar, `float` `Tensor` representing a multiplicative factor\n      used to dampen the proximal gradient descent steps.\n      Default value: `None` (i.e., factor is conceptually `1`).\n    name: Python string representing the name of the TensorFlow operation.\n      The default name is `\"fit_sparse\"`.\n\n  Returns:\n    model_coefficients: (Batch of) `Tensor` of the same shape and dtype as\n      `model_coefficients_start`, representing the computed model coefficients\n      which minimize the regularized negative log-likelihood.\n    is_converged: scalar, `bool` `Tensor` indicating whether the minimization\n      procedure converged across all batches within the specified number of\n      iterations.  Here convergence means that an iteration of the inner loop\n      (`fit_sparse_one_step`) returns `True` for its `is_converged` output\n      value.\n    iter: scalar, `int` `Tensor` indicating the actual number of iterations of\n      the outer loop of the optimizer completed (i.e., number of calls to\n      `fit_sparse_one_step` before achieving convergence).\n\n  #### Example\n\n  ```python\n  from __future__ import print_function\n  import numpy as np\n  import tensorflow as tf\n  import tensorflow_probability as tfp\n  tfd = tfp.distributions\n\n  def make_dataset(n, d, link, scale=1., dtype=np.float32):\n    model_coefficients = tfd.Uniform(\n        low=np.array(-1, dtype), high=np.array(1, dtype)).sample(\n            d, seed=42)\n    radius = np.sqrt(2.)\n    model_coefficients *= radius / tf.linalg.norm(model_coefficients)\n    mask = tf.random_shuffle(tf.range(d)) < tf.to_int32(0.5 * tf.to_float(d))\n    model_coefficients = tf.where(mask, model_coefficients,\n                                  tf.zeros_like(model_coefficients))\n    model_matrix = tfd.Normal(\n        loc=np.array(0, dtype), scale=np.array(1, dtype)).sample(\n            [n, d], seed=43)\n    scale = tf.convert_to_tensor(scale, dtype)\n    linear_response = tf.matmul(model_matrix,\n                                model_coefficients[..., tf.newaxis])[..., 0]\n    if link == 'linear':\n      response = tfd.Normal(loc=linear_response, scale=scale).sample(seed=44)\n    elif link == 'probit':\n      response = tf.cast(\n          tfd.Normal(loc=linear_response, scale=scale).sample(seed=44) > 0,\n                     dtype)\n    elif link == 'logit':\n      response = tfd.Bernoulli(logits=linear_response).sample(seed=44)\n    else:\n      raise ValueError('unrecognized true link: {}'.format(link))\n    return model_matrix, response, model_coefficients, mask\n\n  with tf.Session() as sess:\n    x_, y_, model_coefficients_true_, _ = sess.run(make_dataset(\n        n=int(1e5), d=100, link='probit'))\n\n    model = tfp.glm.Bernoulli()\n    model_coefficients_start = tf.zeros(x_.shape[-1], np.float32)\n\n    model_coefficients, is_converged, num_iter = tfp.glm.fit_sparse(\n        model_matrix=tf.convert_to_tensor(x_),\n        response=tf.convert_to_tensor(y_),\n        model=model,\n        model_coefficients_start=model_coefficients_start,\n        l1_regularizer=800.,\n        l2_regularizer=None,\n        maximum_iterations=10,\n        maximum_full_sweeps_per_iteration=10,\n        tolerance=1e-6,\n        learning_rate=None)\n\n    model_coefficients_, is_converged_, num_iter_ = sess.run([\n        model_coefficients, is_converged, num_iter])\n\n    print(\"is_converged:\", is_converged_)\n    print(\"    num_iter:\", num_iter_)\n    print(\"\\nLearned / True\")\n    print(np.concatenate(\n        [[model_coefficients_], [model_coefficients_true_]], axis=0).T)\n\n  # ==>\n  # is_converged: True\n  #     num_iter: 1\n  #\n  # Learned / True\n  # [[ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.11195257  0.12484948]\n  #  [ 0.          0.        ]\n  #  [ 0.05191106  0.06394956]\n  #  [-0.15090358 -0.15325639]\n  #  [-0.18187316 -0.18825999]\n  #  [-0.06140942 -0.07994166]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.14474444  0.15810856]\n  #  [ 0.          0.        ]\n  #  [-0.25249591 -0.24260855]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [-0.03888761 -0.06755984]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [-0.0192222  -0.04169233]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.01434913  0.03568212]\n  #  [-0.11336883 -0.12873614]\n  #  [ 0.          0.        ]\n  #  [-0.24496339 -0.24048163]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.04088281  0.06565224]\n  #  [-0.12784363 -0.13359821]\n  #  [ 0.05618424  0.07396613]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [ 0.         -0.01719233]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [-0.00076072 -0.03607186]\n  #  [ 0.21801499  0.21146794]\n  #  [-0.02161094 -0.04031265]\n  #  [ 0.0918689   0.10487888]\n  #  [ 0.0106154   0.03233612]\n  #  [-0.07817317 -0.09725142]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [-0.23725343 -0.24194022]\n  #  [ 0.          0.        ]\n  #  [-0.08725718 -0.1048776 ]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [-0.02114314 -0.04145789]\n  #  [ 0.          0.        ]\n  #  [ 0.          0.        ]\n  #  [-0.02710908 -0.04590397]\n  #  [ 0.15293184  0.15415154]\n  #  [ 0.2114463   0.2088728 ]\n  #  [-0.10969634 -0.12368613]\n  #  [ 0.         -0.01505797]\n  #  [-0.01140458 -0.03234904]\n  #  [ 0.16051085  0.1680062 ]\n  #  [ 0.09816848  0.11094204]\n  ```\n\n  #### References\n\n  [1]: Jerome Friedman, Trevor Hastie and Rob Tibshirani. Regularization Paths\n       for Generalized Linear Models via Coordinate Descent. _Journal of\n       Statistical Software_, 33(1), 2010.\n       https://www.jstatsoft.org/article/view/v033i01/v33i01.pdf\n\n  [2]: Guo-Xun Yuan, Chia-Hua Ho and Chih-Jen Lin. An Improved GLMNET for\n       L1-regularized Logistic Regression. _Journal of Machine Learning\n       Research_, 13, 2012.\n       http://www.jmlr.org/papers/volume13/yuan12a/yuan12a.pdf","confidence":0.91,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/glm/proximal_hessian.py#L235-L488","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/glm/proximal_hessian.py#L235-L488","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/glm/proximal_hessian.py#L235-L488","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/glm/proximal_hessian.py#L235-L488"},{"dataset":"csn-python","qid":"813","query":"Build a callable that performs one step of Kalman filtering.\n\n  Args:\n    get_transition_matrix_for_timestep: callable taking a timestep\n      as an integer `Tensor` argument, and returning a `LinearOperator`\n      of shape `[latent_size, latent_size]`.\n    get_transition_noise_for_timestep: callable taking a timestep as\n      an integer `Tensor` argument, and returning a\n      `MultivariateNormalLinearOperator` of event shape\n      `[latent_size]`.\n    get_observation_matrix_for_timestep: callable taking a timestep\n      as an integer `Tensor` argument, and returning a `LinearOperator`\n      of shape `[observation_size, observation_size]`.\n    get_observation_noise_for_timestep: callable taking a timestep as\n      an integer `Tensor` argument, and returning a\n      `MultivariateNormalLinearOperator` of event shape\n      `[observation_size]`.\n\n  Returns:\n    kalman_filter_step: a callable that updates a KalmanFilterState\n      from timestep `t-1` to `t`.","confidence":0.88,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1275-L1394","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1275-L1394","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1275-L1394","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1275-L1394"},{"dataset":"csn-python","qid":"820","query":"Returns a list of binary mask matrices enforcing autoregressivity.","confidence":0.56,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L957-L966","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L957-L966","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L957-L966","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L957-L966"},{"dataset":"csn-python","qid":"821","query":"Attempts to execute TaskInstances that should be executed by the scheduler.\n\n        There are three steps:\n        1. Pick TIs by priority with the constraint that they are in the expected states\n        and that we do exceed max_active_runs or pool limits.\n        2. Change the state for the TIs above atomically.\n        3. Enqueue the TIs in the executor.\n\n        :param simple_dag_bag: TaskInstances associated with DAGs in the\n            simple_dag_bag will be fetched from the DB and executed\n        :type simple_dag_bag: airflow.utils.dag_processing.SimpleDagBag\n        :param states: Execute TaskInstances in these states\n        :type states: tuple[airflow.utils.state.State]\n        :return: Number of task instance with state changed.","confidence":0.84,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1325-L1359","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1325-L1359","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1325-L1359","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1325-L1359"},{"dataset":"csn-python","qid":"823","query":"Create the prior distribution.\n\n  Args:\n    num_topics: Number of topics.\n    initial_value: The starting value for the prior parameters.\n\n  Returns:\n    prior: A `callable` that returns a `tf.distribution.Distribution`\n        instance, the prior distribution.\n    prior_variables: A `list` of `Variable` objects, the trainable parameters\n        of the prior.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L225-L255","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L225-L255","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L225-L255","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L225-L255"},{"dataset":"csn-python","qid":"825","query":"Decompose a forecast distribution into contributions from each component.\n\n  Args:\n    model: An instance of `tfp.sts.Sum` representing a structural time series\n      model.\n    forecast_dist: A `Distribution` instance returned by `tfp.sts.forecast()`.\n      (specifically, must be a `tfd.MixtureSameFamily` over a\n      `tfd.LinearGaussianStateSpaceModel` parameterized by posterior samples).\n    parameter_samples: Python `list` of `Tensors` representing posterior samples\n      of model parameters, with shapes `[concat([[num_posterior_draws],\n      param.prior.batch_shape, param.prior.event_shape]) for param in\n      model.parameters]`. This may optionally also be a map (Python `dict`) of\n      parameter names to `Tensor` values.\n  Returns:\n    component_forecasts: A `collections.OrderedDict` instance mapping\n      component StructuralTimeSeries instances (elements of `model.components`)\n      to `tfd.Distribution` instances representing the marginal forecast for\n      each component. Each distribution has batch and event shape matching\n      `forecast_dist` (specifically, the event shape is\n      `[num_steps_forecast]`).\n\n  #### Examples\n\n  Suppose we've built a model, fit it to data, and constructed a forecast\n  distribution:\n\n  ```python\n    day_of_week = tfp.sts.Seasonal(\n        num_seasons=7,\n        observed_time_series=observed_time_series,\n        name='day_of_week')\n    local_linear_trend = tfp.sts.LocalLinearTrend(\n        observed_time_series=observed_time_series,\n        name='local_linear_trend')\n    model = tfp.sts.Sum(components=[day_of_week, local_linear_trend],\n                        observed_time_series=observed_time_series)\n\n    num_steps_forecast = 50\n    samples, kernel_results = tfp.sts.fit_with_hmc(model, observed_time_series)\n    forecast_dist = tfp.sts.forecast(model, observed_time_series,\n                                 parameter_samples=samples,\n                                 num_steps_forecast=num_steps_forecast)\n  ```\n\n  To extract the forecast for individual components, pass the forecast\n  distribution into `decompose_forecast_by_components`:\n\n  ```python\n    component_forecasts = decompose_forecast_by_component(\n      model, forecast_dist, samples)\n\n    # Component mean and stddev have shape `[num_steps_forecast]`.\n    day_of_week_effect_mean = forecast_components[day_of_week].mean()\n    day_of_week_effect_stddev = forecast_components[day_of_week].stddev()\n  ```\n\n  Using the component forecasts, we can visualize the uncertainty for each\n  component:\n\n  ```\n  from matplotlib import pylab as plt\n  num_components = len(component_forecasts)\n  xs = np.arange(num_steps_forecast)\n  fig = plt.figure(figsize=(12, 3 * num_components))\n  for i, (component, component_dist) in enumerate(component_forecasts.items()):\n\n    # If in graph mode, replace `.numpy()` with `.eval()` or `sess.run()`.\n    component_mean = component_dist.mean().numpy()\n    component_stddev = component_dist.stddev().numpy()\n\n    ax = fig.add_subplot(num_components, 1, 1 + i)\n    ax.plot(xs, component_mean, lw=2)\n    ax.fill_between(xs,\n                    component_mean - 2 * component_stddev,\n                    component_mean + 2 * component_stddev,\n                    alpha=0.5)\n    ax.set_title(component.name)\n  ```","confidence":0.91,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/decomposition.py#L222-L325","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/decomposition.py#L222-L325","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/decomposition.py#L222-L325","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/decomposition.py#L222-L325"},{"dataset":"csn-python","qid":"828","query":"Creates a function to build Normal distributions with trainable params.\n\n  This function produces a closure which produces `tfd.Normal`\n  parameterized by a loc` and `scale` each created using `tf.get_variable`.\n\n  Args:\n    is_singular: Python `bool` if `True`, forces the special case limit of\n      `scale->0`, i.e., a `Deterministic` distribution.\n    loc_initializer: Initializer function for the `loc` parameters.\n      The default is `tf.random_normal_initializer(mean=0., stddev=0.1)`.\n    untransformed_scale_initializer: Initializer function for the `scale`\n      parameters. Default value: `tf.random_normal_initializer(mean=-3.,\n      stddev=0.1)`. This implies the softplus transformed result is initialized\n      near `0`. It allows a `Normal` distribution with `scale` parameter set to\n      this value to approximately act like a point mass.\n    loc_regularizer: Regularizer function for the `loc` parameters.\n    untransformed_scale_regularizer: Regularizer function for the `scale`\n      parameters.\n    loc_constraint: An optional projection function to be applied to the\n      loc after being updated by an `Optimizer`. The function must take as input\n      the unprojected variable and must return the projected variable (which\n      must have the same shape). Constraints are not safe to use when doing\n      asynchronous distributed training.\n    untransformed_scale_constraint: An optional projection function to be\n      applied to the `scale` parameters after being updated by an `Optimizer`\n      (e.g. used to implement norm constraints or value constraints). The\n      function must take as input the unprojected variable and must return the\n      projected variable (which must have the same shape). Constraints are not\n      safe to use when doing asynchronous distributed training.\n\n  Returns:\n    make_normal_fn: Python `callable` which creates a `tfd.Normal`\n      using from args: `dtype, shape, name, trainable, add_variable_fn`.","confidence":0.89,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/util.py#L119-L193","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/util.py#L119-L193","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/util.py#L119-L193","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/util.py#L119-L193"},{"dataset":"csn-python","qid":"83","query":"Creates Operators needed for model evaluation and returns.\n\n    It gets prediction over inputs via Cloud ML Engine BatchPrediction API by\n    calling MLEngineBatchPredictionOperator, then summarize and validate\n    the result via Cloud Dataflow using DataFlowPythonOperator.\n\n    For details and pricing about Batch prediction, please refer to the website\n    https://cloud.google.com/ml-engine/docs/how-tos/batch-predict\n    and for Cloud Dataflow, https://cloud.google.com/dataflow/docs/\n\n    It returns three chained operators for prediction, summary, and validation,\n    named as <prefix>-prediction, <prefix>-summary, and <prefix>-validation,\n    respectively.\n    (<prefix> should contain only alphanumeric characters or hyphen.)\n\n    The upstream and downstream can be set accordingly like:\n      pred, _, val = create_evaluate_ops(...)\n      pred.set_upstream(upstream_op)\n      ...\n      downstream_op.set_upstream(val)\n\n    Callers will provide two python callables, metric_fn and validate_fn, in\n    order to customize the evaluation behavior as they wish.\n    - metric_fn receives a dictionary per instance derived from json in the\n      batch prediction result. The keys might vary depending on the model.\n      It should return a tuple of metrics.\n    - validation_fn receives a dictionary of the averaged metrics that metric_fn\n      generated over all instances.\n      The key/value of the dictionary matches to what's given by\n      metric_fn_and_keys arg.\n      The dictionary contains an additional metric, 'count' to represent the\n      total number of instances received for evaluation.\n      The function would raise an exception to mark the task as failed, in a\n      case the validation result is not okay to proceed (i.e. to set the trained\n      version as default).\n\n    Typical examples are like this:\n\n    def get_metric_fn_and_keys():\n        import math  # imports should be outside of the metric_fn below.\n        def error_and_squared_error(inst):\n            label = float(inst['input_label'])\n            classes = float(inst['classes'])  # 0 or 1\n            err = abs(classes-label)\n            squared_err = math.pow(classes-label, 2)\n            return (err, squared_err)  # returns a tuple.\n        return error_and_squared_error, ['err', 'mse']  # key order must match.\n\n    def validate_err_and_count(summary):\n        if summary['err'] > 0.2:\n            raise ValueError('Too high err>0.2; summary=%s' % summary)\n        if summary['mse'] > 0.05:\n            raise ValueError('Too high mse>0.05; summary=%s' % summary)\n        if summary['count'] < 1000:\n            raise ValueError('Too few instances<1000; summary=%s' % summary)\n        return summary\n\n    For the details on the other BatchPrediction-related arguments (project_id,\n    job_id, region, data_format, input_paths, prediction_path, model_uri),\n    please refer to MLEngineBatchPredictionOperator too.\n\n    :param task_prefix: a prefix for the tasks. Only alphanumeric characters and\n        hyphen are allowed (no underscores), since this will be used as dataflow\n        job name, which doesn't allow other characters.\n    :type task_prefix: str\n\n    :param data_format: either of 'TEXT', 'TF_RECORD', 'TF_RECORD_GZIP'\n    :type data_format: str\n\n    :param input_paths: a list of input paths to be sent to BatchPrediction.\n    :type input_paths: list[str]\n\n    :param prediction_path: GCS path to put the prediction results in.\n    :type prediction_path: str\n\n    :param metric_fn_and_keys: a tuple of metric_fn and metric_keys:\n        - metric_fn is a function that accepts a dictionary (for an instance),\n          and returns a tuple of metric(s) that it calculates.\n        - metric_keys is a list of strings to denote the key of each metric.\n    :type metric_fn_and_keys: tuple of a function and a list[str]\n\n    :param validate_fn: a function to validate whether the averaged metric(s) is\n        good enough to push the model.\n    :type validate_fn: function\n\n    :param batch_prediction_job_id: the id to use for the Cloud ML Batch\n        prediction job. Passed directly to the MLEngineBatchPredictionOperator as\n        the job_id argument.\n    :type batch_prediction_job_id: str\n\n    :param project_id: the Google Cloud Platform project id in which to execute\n        Cloud ML Batch Prediction and Dataflow jobs. If None, then the `dag`'s\n        `default_args['project_id']` will be used.\n    :type project_id: str\n\n    :param region: the Google Cloud Platform region in which to execute Cloud ML\n        Batch Prediction and Dataflow jobs. If None, then the `dag`'s\n        `default_args['region']` will be used.\n    :type region: str\n\n    :param dataflow_options: options to run Dataflow jobs. If None, then the\n        `dag`'s `default_args['dataflow_default_options']` will be used.\n    :type dataflow_options: dictionary\n\n    :param model_uri: GCS path of the model exported by Tensorflow using\n        tensorflow.estimator.export_savedmodel(). It cannot be used with\n        model_name or version_name below. See MLEngineBatchPredictionOperator for\n        more detail.\n    :type model_uri: str\n\n    :param model_name: Used to indicate a model to use for prediction. Can be\n        used in combination with version_name, but cannot be used together with\n        model_uri. See MLEngineBatchPredictionOperator for more detail. If None,\n        then the `dag`'s `default_args['model_name']` will be used.\n    :type model_name: str\n\n    :param version_name: Used to indicate a model version to use for prediction,\n        in combination with model_name. Cannot be used together with model_uri.\n        See MLEngineBatchPredictionOperator for more detail. If None, then the\n        `dag`'s `default_args['version_name']` will be used.\n    :type version_name: str\n\n    :param dag: The `DAG` to use for all Operators.\n    :type dag: airflow.models.DAG\n\n    :returns: a tuple of three operators, (prediction, summary, validation)\n    :rtype: tuple(DataFlowPythonOperator, DataFlowPythonOperator,\n                  PythonOperator)","confidence":0.84,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/utils/mlengine_operator_utils.py#L32-L246","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/utils/mlengine_operator_utils.py#L32-L246","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/utils/mlengine_operator_utils.py#L32-L246","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/utils/mlengine_operator_utils.py#L32-L246"},{"dataset":"csn-python","qid":"830","query":"Get results (or just s3 locations) of a command from Qubole and save into a file\n        :param ti: Task Instance of the dag, used to determine the Quboles command id\n        :param fp: Optional file pointer, will create one and return if None passed\n        :param inline: True to download actual results, False to get s3 locations only\n        :param delim: Replaces the CTL-A chars with the given delim, defaults to ','\n        :param fetch: when inline is True, get results directly from s3 (if large)\n        :return: file location containing actual results or s3 locations of results","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L164-L190","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L164-L190","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L164-L190","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L164-L190"},{"dataset":"csn-python","qid":"834","query":"Runs one step of leapfrog integration.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/no_u_turn_sampler/nuts.py#L478-L500","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/no_u_turn_sampler/nuts.py#L478-L500","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/no_u_turn_sampler/nuts.py#L478-L500","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/no_u_turn_sampler/nuts.py#L478-L500"},{"dataset":"csn-python","qid":"835","query":"Execute sqoop job","confidence":0.69,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/sqoop_operator.py#L166-L234","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/sqoop_operator.py#L166-L234","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/sqoop_operator.py#L166-L234","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/sqoop_operator.py#L166-L234"},{"dataset":"csn-python","qid":"838","query":"Set the dag run for a specific execution date and its running task instances\n    to failed.\n    :param dag: the DAG of which to alter state\n    :param execution_date: the execution date from which to start looking\n    :param commit: commit DAG and tasks to be altered to the database\n    :param session: database session\n    :return: If commit is true, list of tasks that have been updated,\n             otherwise list of tasks that will be updated\n    :raises: AssertionError if dag or execution_date is invalid","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L243-L280","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L243-L280","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L243-L280","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/mark_tasks.py#L243-L280"},{"dataset":"csn-python","qid":"839","query":"Implements Markov chain Monte Carlo via repeated `TransitionKernel` steps.\n\n  This function samples from an Markov chain at `current_state` and whose\n  stationary distribution is governed by the supplied `TransitionKernel`\n  instance (`kernel`).\n\n  This function can sample from multiple chains, in parallel. (Whether or not\n  there are multiple chains is dictated by the `kernel`.)\n\n  The `current_state` can be represented as a single `Tensor` or a `list` of\n  `Tensors` which collectively represent the current state.\n\n  Since MCMC states are correlated, it is sometimes desirable to produce\n  additional intermediate states, and then discard them, ending up with a set of\n  states with decreased autocorrelation.  See [Owen (2017)][1]. Such \"thinning\"\n  is made possible by setting `num_steps_between_results > 0`. The chain then\n  takes `num_steps_between_results` extra steps between the steps that make it\n  into the results. The extra steps are never materialized (in calls to\n  `sess.run`), and thus do not increase memory requirements.\n\n  Warning: when setting a `seed` in the `kernel`, ensure that `sample_chain`'s\n  `parallel_iterations=1`, otherwise results will not be reproducible.\n\n  In addition to returning the chain state, this function supports tracing of\n  auxiliary variables used by the kernel. The traced values are selected by\n  specifying `trace_fn`. By default, all kernel results are traced but in the\n  future the default will be changed to no results being traced, so plan\n  accordingly. See below for some examples of this feature.\n\n  Args:\n    num_results: Integer number of Markov chain draws.\n    current_state: `Tensor` or Python `list` of `Tensor`s representing the\n      current state(s) of the Markov chain(s).\n    previous_kernel_results: A `Tensor` or a nested collection of `Tensor`s\n      representing internal calculations made within the previous call to this\n      function (or as returned by `bootstrap_results`).\n    kernel: An instance of `tfp.mcmc.TransitionKernel` which implements one step\n      of the Markov chain.\n    num_burnin_steps: Integer number of chain steps to take before starting to\n      collect results.\n      Default value: 0 (i.e., no burn-in).\n    num_steps_between_results: Integer number of chain steps between collecting\n      a result. Only one out of every `num_steps_between_samples + 1` steps is\n      included in the returned results.  The number of returned chain states is\n      still equal to `num_results`.  Default value: 0 (i.e., no thinning).\n    trace_fn: A callable that takes in the current chain state and the previous\n      kernel results and return a `Tensor` or a nested collection of `Tensor`s\n      that is then traced along with the chain state.\n    return_final_kernel_results: If `True`, then the final kernel results are\n      returned alongside the chain state and the trace specified by the\n      `trace_fn`.\n    parallel_iterations: The number of iterations allowed to run in parallel. It\n      must be a positive integer. See `tf.while_loop` for more details.\n    name: Python `str` name prefixed to Ops created by this function.\n      Default value: `None` (i.e., \"mcmc_sample_chain\").\n\n  Returns:\n    checkpointable_states_and_trace: if `return_final_kernel_results` is\n      `True`. The return value is an instance of\n      `CheckpointableStatesAndTrace`.\n    all_states: if `return_final_kernel_results` is `False` and `trace_fn` is\n      `None`. The return value is a `Tensor` or Python list of `Tensor`s\n      representing the state(s) of the Markov chain(s) at each result step. Has\n      same shape as input `current_state` but with a prepended\n      `num_results`-size dimension.\n    states_and_trace: if `return_final_kernel_results` is `False` and\n      `trace_fn` is not `None`. The return value is an instance of\n      `StatesAndTrace`.\n\n  #### Examples\n\n  ##### Sample from a diagonal-variance Gaussian.\n\n  I.e.,\n\n  ```none\n  for i=1..n:\n    x[i] ~ MultivariateNormal(loc=0, scale=diag(true_stddev))  # likelihood\n  ```\n\n  ```python\n  import tensorflow as tf\n  import tensorflow_probability as tfp\n  tfd = tfp.distributions\n\n  dims = 10\n  true_stddev = np.sqrt(np.linspace(1., 3., dims))\n  likelihood = tfd.MultivariateNormalDiag(loc=0., scale_diag=true_stddev)\n\n  states = tfp.mcmc.sample_chain(\n      num_results=1000,\n      num_burnin_steps=500,\n      current_state=tf.zeros(dims),\n      kernel=tfp.mcmc.HamiltonianMonteCarlo(\n        target_log_prob_fn=likelihood.log_prob,\n        step_size=0.5,\n        num_leapfrog_steps=2),\n      trace_fn=None)\n\n  sample_mean = tf.reduce_mean(states, axis=0)\n  # ==> approx all zeros\n\n  sample_stddev = tf.sqrt(tf.reduce_mean(\n      tf.squared_difference(states, sample_mean),\n      axis=0))\n  # ==> approx equal true_stddev\n  ```\n\n  ##### Sampling from factor-analysis posteriors with known factors.\n\n  I.e.,\n\n  ```none\n  # prior\n  w ~ MultivariateNormal(loc=0, scale=eye(d))\n  for i=1..n:\n    # likelihood\n    x[i] ~ Normal(loc=w^T F[i], scale=1)\n  ```\n\n  where `F` denotes factors.\n\n  ```python\n  import tensorflow as tf\n  import tensorflow_probability as tfp\n  tfd = tfp.distributions\n\n  # Specify model.\n  def make_prior(dims):\n    return tfd.MultivariateNormalDiag(\n        loc=tf.zeros(dims))\n\n  def make_likelihood(weights, factors):\n    return tfd.MultivariateNormalDiag(\n        loc=tf.matmul(weights, factors, adjoint_b=True))\n\n  def joint_log_prob(num_weights, factors, x, w):\n    return (make_prior(num_weights).log_prob(w) +\n            make_likelihood(w, factors).log_prob(x))\n\n  def unnormalized_log_posterior(w):\n    # Posterior is proportional to: `p(W, X=x | factors)`.\n    return joint_log_prob(num_weights, factors, x, w)\n\n  # Setup data.\n  num_weights = 10 # == d\n  num_factors = 40 # == n\n  num_chains = 100\n\n  weights = make_prior(num_weights).sample(1)\n  factors = tf.random_normal([num_factors, num_weights])\n  x = make_likelihood(weights, factors).sample()\n\n  # Sample from Hamiltonian Monte Carlo Markov Chain.\n\n  # Get `num_results` samples from `num_chains` independent chains.\n  chains_states, kernels_results = tfp.mcmc.sample_chain(\n      num_results=1000,\n      num_burnin_steps=500,\n      current_state=tf.zeros([num_chains, num_weights], name='init_weights'),\n      kernel=tfp.mcmc.HamiltonianMonteCarlo(\n        target_log_prob_fn=unnormalized_log_posterior,\n        step_size=0.1,\n        num_leapfrog_steps=2))\n\n  # Compute sample stats.\n  sample_mean = tf.reduce_mean(chains_states, axis=[0, 1])\n  # ==> approx equal to weights\n\n  sample_var = tf.reduce_mean(\n      tf.squared_difference(chains_states, sample_mean),\n      axis=[0, 1])\n  # ==> less than 1\n  ```\n\n  ##### Custom tracing functions.\n\n  ```python\n  import tensorflow as tf\n  import tensorflow_probability as tfp\n  tfd = tfp.distributions\n\n  likelihood = tfd.Normal(loc=0., scale=1.)\n\n  def sample_chain(trace_fn):\n    return tfp.mcmc.sample_chain(\n      num_results=1000,\n      num_burnin_steps=500,\n      current_state=0.,\n      kernel=tfp.mcmc.HamiltonianMonteCarlo(\n        target_log_prob_fn=likelihood.log_prob,\n        step_size=0.5,\n        num_leapfrog_steps=2),\n      trace_fn=trace_fn)\n\n  def trace_log_accept_ratio(states, previous_kernel_results):\n    return previous_kernel_results.log_accept_ratio\n\n  def trace_everything(states, previous_kernel_results):\n    return previous_kernel_results\n\n  _, log_accept_ratio = sample_chain(trace_fn=trace_log_accept_ratio)\n  _, kernel_results = sample_chain(trace_fn=trace_everything)\n\n  acceptance_prob = tf.exp(tf.minimum(log_accept_ratio_, 0.))\n  # Equivalent to, but more efficient than:\n  acceptance_prob = tf.exp(tf.minimum(kernel_results.log_accept_ratio_, 0.))\n  ```\n\n  #### References\n\n  [1]: Art B. Owen. Statistically efficient thinning of a Markov chain sampler.\n       _Technical Report_, 2017.\n       http://statweb.stanford.edu/~owen/reports/bestthinning.pdf","confidence":0.87,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/sample.py#L81-L372","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/sample.py#L81-L372","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/sample.py#L81-L372","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/sample.py#L81-L372"},{"dataset":"csn-python","qid":"840","query":"Returns whether or not this dependency is met for a given task instance. A\n        dependency is considered met if all of the dependency statuses it reports are\n        passing.\n\n        :param ti: the task instance to see if this dependency is met for\n        :type ti: airflow.models.TaskInstance\n        :param session: database session\n        :type session: sqlalchemy.orm.session.Session\n        :param dep_context: The context this dependency is being checked under that stores\n            state that can be used by this dependency.\n        :type dep_context: BaseDepContext","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L110-L125","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L110-L125","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L110-L125","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L110-L125"},{"dataset":"csn-python","qid":"841","query":"Trigger a new dag run for a Dag with an execution date of now unless\n    specified in the data.","confidence":0.83,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L47-L92","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L47-L92","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L47-L92","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L47-L92"},{"dataset":"csn-python","qid":"847","query":"Gets a list of snapshots for a cluster\n\n        :param cluster_identifier: unique identifier of a cluster\n        :type cluster_identifier: str","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L68-L83","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L68-L83","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L68-L83","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L68-L83"},{"dataset":"csn-python","qid":"849","query":"r\"\"\"Finds root(s) of a function of single variable using the secant method.\n\n  The [secant method](https://en.wikipedia.org/wiki/Secant_method) is a\n  root-finding algorithm that uses a succession of roots of secant lines to\n  better approximate a root of a function. The secant method can be thought of\n  as a finite-difference approximation of Newton's method.\n\n  Args:\n    objective_fn: Python callable for which roots are searched. It must be a\n      callable of a single variable. `objective_fn` must return a `Tensor` of\n      the same shape and dtype as `initial_position`.\n    initial_position: `Tensor` or Python float representing the starting\n      position. The function will search for roots in the neighborhood of each\n      point. The shape of `initial_position` should match that of the input to\n      `objective_fn`.\n    next_position: Optional `Tensor` representing the next position in the\n      search. If specified, this argument must broadcast with the shape of\n      `initial_position` and have the same dtype. It will be used to compute the\n      first step to take when searching for roots. If not specified, a default\n      value will be used instead.\n      Default value: `initial_position * (1 + 1e-4) + sign(initial_position) *\n        1e-4`.\n    value_at_position: Optional `Tensor` or Pyhon float representing the value\n      of `objective_fn` at `initial_position`. If specified, this argument must\n      have the same shape and dtype as `initial_position`. If not specified, the\n      value will be evaluated during the search.\n      Default value: None.\n    position_tolerance: Optional `Tensor` representing the tolerance for the\n      estimated roots. If specified, this argument must broadcast with the shape\n      of `initial_position` and have the same dtype.\n      Default value: `1e-8`.\n    value_tolerance: Optional `Tensor` representing the tolerance used to check\n      for roots. If the absolute value of `objective_fn` is smaller than\n      `value_tolerance` at a given position, then that position is considered a\n      root for the function. If specified, this argument must broadcast with the\n      shape of `initial_position` and have the same dtype.\n      Default value: `1e-8`.\n    max_iterations: Optional `Tensor` or Python integer specifying the maximum\n      number of steps to perform for each initial position. Must broadcast with\n      the shape of `initial_position`.\n      Default value: `50`.\n    stopping_policy_fn: Python `callable` controlling the algorithm termination.\n      It must be a callable accepting a `Tensor` of booleans with the shape of\n      `initial_position` (each denoting whether the search is finished for each\n      starting point), and returning a scalar boolean `Tensor` (indicating\n      whether the overall search should stop). Typical values are\n      `tf.reduce_all` (which returns only when the search is finished for all\n      points), and `tf.reduce_any` (which returns as soon as the search is\n      finished for any point).\n      Default value: `tf.reduce_all` (returns only when the search is finished\n        for all points).\n    validate_args: Python `bool` indicating whether to validate arguments such\n      as `position_tolerance`, `value_tolerance`, and `max_iterations`.\n      Default value: `False`.\n    name: Python `str` name prefixed to ops created by this function.\n\n  Returns:\n    root_search_results: A Python `namedtuple` containing the following items:\n      estimated_root: `Tensor` containing the last position explored. If the\n        search was successful within the specified tolerance, this position is\n        a root of the objective function.\n      objective_at_estimated_root: `Tensor` containing the value of the\n        objective function at `position`. If the search was successful within\n        the specified tolerance, then this is close to 0.\n      num_iterations: The number of iterations performed.\n\n  Raises:\n    ValueError: if a non-callable `stopping_policy_fn` is passed.\n\n  #### Examples\n\n  ```python\n  import tensorflow as tf\n  import tensorflow_probability as tfp\n  tf.enable_eager_execution()\n\n  # Example 1: Roots of a single function from two different starting points.\n\n  f = lambda x: (63 * x**5 - 70 * x**3 + 15 * x) / 8.\n  x = tf.constant([-1, 10], dtype=tf.float64)\n\n  tfp.math.secant_root(objective_fn=f, initial_position=x))\n  # ==> RootSearchResults(\n      estimated_root=array([-0.90617985, 0.90617985]),\n      objective_at_estimated_root=array([-4.81727769e-10, 7.44957651e-10]),\n      num_iterations=array([ 7, 24], dtype=int32))\n\n  tfp.math.secant_root(objective_fn=f,\n                       initial_position=x,\n                       stopping_policy_fn=tf.reduce_any)\n  # ==> RootSearchResults(\n      estimated_root=array([-0.90617985, 3.27379206]),\n      objective_at_estimated_root=array([-4.81727769e-10, 2.66058312e+03]),\n      num_iterations=array([7, 8], dtype=int32))\n\n  # Example 2: Roots of a multiplex function from a single starting point.\n\n  def f(x):\n    return tf.constant([0., 63. / 8], dtype=tf.float64) * x**5 \\\n        + tf.constant([5. / 2, -70. / 8], dtype=tf.float64) * x**3 \\\n        + tf.constant([-3. / 2, 15. / 8], dtype=tf.float64) * x\n\n  x = tf.constant([-1, -1], dtype=tf.float64)\n\n  tfp.math.secant_root(objective_fn=f, initial_position=x)\n  # ==> RootSearchResults(\n      estimated_root=array([-0.77459667, -0.90617985]),\n      objective_at_estimated_root=array([-7.81339438e-11, -4.81727769e-10]),\n      num_iterations=array([7, 7], dtype=int32))\n\n  # Example 3: Roots of a multiplex function from two starting points.\n\n  def f(x):\n    return tf.constant([0., 63. / 8], dtype=tf.float64) * x**5 \\\n        + tf.constant([5. / 2, -70. / 8], dtype=tf.float64) * x**3 \\\n        + tf.constant([-3. / 2, 15. / 8], dtype=tf.float64) * x\n\n  x = tf.constant([[-1, -1], [10, 10]], dtype=tf.float64)\n\n  tfp.math.secant_root(objective_fn=f, initial_position=x)\n  # ==> RootSearchResults(\n      estimated_root=array([\n          [-0.77459667, -0.90617985],\n          [ 0.77459667, 0.90617985]]),\n      objective_at_estimated_root=array([\n          [-7.81339438e-11, -4.81727769e-10],\n          [6.66025013e-11, 7.44957651e-10]]),\n      num_iterations=array([\n          [7, 7],\n          [16, 24]], dtype=int32))\n  ```","confidence":0.91,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/root_search.py#L44-L321","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/root_search.py#L44-L321","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/root_search.py#L44-L321","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/root_search.py#L44-L321"},{"dataset":"csn-python","qid":"85","query":"Load AirflowPlugin subclasses from the entrypoints\n    provided. The entry_point group should be 'airflow.plugins'.\n\n    :param entry_points: A collection of entrypoints to search for plugins\n    :type entry_points: Generator[setuptools.EntryPoint, None, None]\n    :param airflow_plugins: A collection of existing airflow plugins to\n        ensure we don't load duplicates\n    :type airflow_plugins: list[type[airflow.plugins_manager.AirflowPlugin]]\n    :rtype: list[airflow.plugins_manager.AirflowPlugin]","confidence":0.85,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L79-L98","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L79-L98","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L79-L98","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/plugins_manager.py#L79-L98"},{"dataset":"csn-python","qid":"856","query":"Create a transform job\n\n        :param config: the config for transform job\n        :type config: dict\n        :param wait_for_completion: if the program should keep running until job finishes\n        :type wait_for_completion: bool\n        :param check_interval: the time interval in seconds which the operator\n            will check the status of any SageMaker job\n        :type check_interval: int\n        :param max_ingestion_time: the maximum ingestion time in seconds. Any\n            SageMaker jobs that run longer than this will fail. Setting this to\n            None implies no timeout for any SageMaker job.\n        :type max_ingestion_time: int\n        :return: A response to transform job creation","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L398-L426","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L398-L426","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L398-L426","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L398-L426"},{"dataset":"csn-python","qid":"861","query":"Calculate the batched KL divergence KL(g0 || g1) with g0 and g1 Gamma.\n\n  Args:\n    g0: instance of a Gamma distribution object.\n    g1: instance of a Gamma distribution object.\n    name: (optional) Name to use for created operations. Default is\n      \"kl_gamma_gamma\".\n\n  Returns:\n    kl_gamma_gamma: `Tensor`. The batchwise KL(g0 || g1).","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/gamma.py#L273-L296","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/gamma.py#L273-L296","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/gamma.py#L273-L296","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/gamma.py#L273-L296"},{"dataset":"csn-python","qid":"863","query":"Build transition noise distribution for a ConstrainedSeasonalSSM.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/seasonal.py#L628-L691","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/seasonal.py#L628-L691","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/seasonal.py#L628-L691","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/seasonal.py#L628-L691"},{"dataset":"csn-python","qid":"865","query":"Create the encoder function.\n\n  Args:\n    activation: Activation function to use.\n    num_topics: The number of topics.\n    layer_sizes: The number of hidden units per layer in the encoder.\n\n  Returns:\n    encoder: A `callable` mapping a bag-of-words `Tensor` to a\n      `tfd.Distribution` instance over topics.","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L164-L194","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L164-L194","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L164-L194","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py#L164-L194"},{"dataset":"csn-python","qid":"866","query":"Builds fake MNIST-style data for unit testing.","confidence":0.84,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vae.py#L462-L476","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vae.py#L462-L476","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vae.py#L462-L476","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/examples/vae.py#L462-L476"},{"dataset":"csn-python","qid":"87","query":"Create a pool.","confidence":0.94,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L45-L66","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L45-L66","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L45-L66","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/pool.py#L45-L66"},{"dataset":"csn-python","qid":"873","query":"Build a callable to push latent means/covs to observed means/covs.\n\n  Args:\n    get_observation_matrix_for_timestep: callable taking a timestep\n      as an integer `Tensor` argument, and returning a `LinearOperator`\n      of shape `[observation_size, observation_size]`.\n    get_observation_noise_for_timestep: callable taking a timestep as\n      an integer `Tensor` argument, and returning a\n      `MultivariateNormalLinearOperator` of event shape\n      `[observation_size]`.\n\n  Returns:\n    pushforward_latents_step: a callable that computes the observation mean and\n    covariance at time `t`, given latent mean and covariance at time `t`.","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1688-L1721","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1688-L1721","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1688-L1721","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1688-L1721"},{"dataset":"csn-python","qid":"875","query":"Infer distribution batch and event shapes from a location and scale.\n\n  Location and scale family distributions determine their batch/event shape by\n  broadcasting the `loc` and `scale` args.  This helper does that broadcast,\n  statically if possible.\n\n  Batch shape broadcasts as per the normal rules.\n  We allow the `loc` event shape to broadcast up to that of `scale`.  We do not\n  allow `scale`'s event shape to change.  Therefore, the last dimension of `loc`\n  must either be size `1`, or the same as `scale.range_dimension`.\n\n  See `MultivariateNormalLinearOperator` for a usage example.\n\n  Args:\n    loc: `Tensor` (already converted to tensor) or `None`. If `None`, or\n      `rank(loc)==0`, both batch and event shape are determined by `scale`.\n    scale:  A `LinearOperator` instance.\n    name:  A string name to prepend to created ops.\n\n  Returns:\n    batch_shape:  `TensorShape` (if broadcast is done statically), or `Tensor`.\n    event_shape:  `TensorShape` (if broadcast is done statically), or `Tensor`.\n\n  Raises:\n    ValueError:  If the last dimension of `loc` is determined statically to be\n      different than the range of `scale`.","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L281-L351","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L281-L351","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L281-L351","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L281-L351"},{"dataset":"csn-python","qid":"877","query":"Returns rejection samples from trying to get good correlation matrices.\n\n  The proposal being rejected from is the uniform distribution on\n  \"correlation-like\" matrices.  We say a matrix is \"correlation-like\"\n  if it is a symmetric square matrix with all entries between -1 and 1\n  (inclusive) and 1s on the main diagonal.  Of these, the ones that\n  are positive semi-definite are exactly the correlation matrices.\n\n  The rejection algorithm, then, is to sample a `Tensor` of\n  `sample_shape` correlation-like matrices of dimensions `dim` by\n  `dim`, and check each one for (i) being a correlation matrix (i.e.,\n  PSD), and (ii) having determinant at least the corresponding entry\n  of `det_bounds`.\n\n  Args:\n    det_bounds: A `Tensor` of lower bounds on the determinants of\n      acceptable matrices.  The shape must broadcast with `sample_shape`.\n    dim: A Python `int` dimension of correlation matrices to sample.\n    sample_shape: Python `tuple` of `int` shape of the samples to\n      compute, excluding the two matrix dimensions.\n    dtype: The `dtype` in which to do the computation.\n    seed: Random seed.\n\n  Returns:\n    weights: A `Tensor` of shape `sample_shape`.  Each entry is 0 if the\n      corresponding matrix was not a correlation matrix, or had too\n      small of a determinant.  Otherwise, the entry is the\n      multiplicative inverse of the density of proposing that matrix\n      uniformly, i.e., the volume of the set of `dim` by `dim`\n      correlation-like matrices.\n    volume: The volume of the set of `dim` by `dim` correlation-like\n      matrices.","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L172-L216","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L172-L216","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L172-L216","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L172-L216"},{"dataset":"csn-python","qid":"879","query":"Validate and return float type based on `tensors` and `dtype`.\n\n  For ops such as matrix multiplication, inputs and weights must be of the\n  same float type. This function validates that all `tensors` are the same type,\n  validates that type is `dtype` (if supplied), and returns the type. Type must\n  be a floating point type. If neither `tensors` nor `dtype` is supplied,\n  the function will return `dtypes.float32`.\n\n  Args:\n    tensors: Tensors of input values. Can include `None` elements, which will\n      be ignored.\n    dtype: Expected type.\n\n  Returns:\n    Validated type.\n\n  Raises:\n    ValueError: if neither `tensors` nor `dtype` is supplied, or result is not\n      float, or the common type of the inputs is not a floating point type.","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L201-L228","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L201-L228","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L201-L228","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L201-L228"},{"dataset":"csn-python","qid":"880","query":"Applies the L-BFGS algorithm to minimize a differentiable function.\n\n  Performs unconstrained minimization of a differentiable function using the\n  L-BFGS scheme. See [Nocedal and Wright(2006)][1] for details of the algorithm.\n\n  ### Usage:\n\n  The following example demonstrates the L-BFGS optimizer attempting to find the\n  minimum for a simple high-dimensional quadratic objective function.\n\n  ```python\n    # A high-dimensional quadratic bowl.\n    ndims = 60\n    minimum = np.ones([ndims], dtype='float64')\n    scales = np.arange(ndims, dtype='float64') + 1.0\n\n    # The objective function and the gradient.\n    def quadratic(x):\n      value = tf.reduce_sum(scales * (x - minimum) ** 2)\n      return value, tf.gradients(value, x)[0]\n\n    start = np.arange(ndims, 0, -1, dtype='float64')\n    optim_results = tfp.optimizer.lbfgs_minimize(\n        quadratic, initial_position=start, num_correction_pairs=10,\n        tolerance=1e-8)\n\n    with tf.Session() as session:\n      results = session.run(optim_results)\n      # Check that the search converged\n      assert(results.converged)\n      # Check that the argmin is close to the actual value.\n      np.testing.assert_allclose(results.position, minimum)\n  ```\n\n  ### References:\n\n  [1] Jorge Nocedal, Stephen Wright. Numerical Optimization. Springer Series\n      in Operations Research. pp 176-180. 2006\n\n  http://pages.mtu.edu/~struther/Courses/OLD/Sp2013/5630/Jorge_Nocedal_Numerical_optimization_267490.pdf\n\n  Args:\n    value_and_gradients_function:  A Python callable that accepts a point as a\n      real `Tensor` and returns a tuple of `Tensor`s of real dtype containing\n      the value of the function and its gradient at that point. The function\n      to be minimized. The input is of shape `[..., n]`, where `n` is the size\n      of the domain of input points, and all others are batching dimensions.\n      The first component of the return value is a real `Tensor` of matching\n      shape `[...]`. The second component (the gradient) is also of shape\n      `[..., n]` like the input value to the function.\n    initial_position: Real `Tensor` of shape `[..., n]`. The starting point, or\n      points when using batching dimensions, of the search procedure. At these\n      points the function value and the gradient norm should be finite.\n    num_correction_pairs: Positive integer. Specifies the maximum number of\n      (position_delta, gradient_delta) correction pairs to keep as implicit\n      approximation of the Hessian matrix.\n    tolerance: Scalar `Tensor` of real dtype. Specifies the gradient tolerance\n      for the procedure. If the supremum norm of the gradient vector is below\n      this number, the algorithm is stopped.\n    x_tolerance: Scalar `Tensor` of real dtype. If the absolute change in the\n      position between one iteration and the next is smaller than this number,\n      the algorithm is stopped.\n    f_relative_tolerance: Scalar `Tensor` of real dtype. If the relative change\n      in the objective value between one iteration and the next is smaller\n      than this value, the algorithm is stopped.\n    initial_inverse_hessian_estimate: None. Option currently not supported.\n    max_iterations: Scalar positive int32 `Tensor`. The maximum number of\n      iterations for L-BFGS updates.\n    parallel_iterations: Positive integer. The number of iterations allowed to\n      run in parallel.\n    stopping_condition: (Optional) A Python function that takes as input two\n      Boolean tensors of shape `[...]`, and returns a Boolean scalar tensor.\n      The input tensors are `converged` and `failed`, indicating the current\n      status of each respective batch member; the return value states whether\n      the algorithm should stop. The default is tfp.optimizer.converged_all\n      which only stops when all batch members have either converged or failed.\n      An alternative is tfp.optimizer.converged_any which stops as soon as one\n      batch member has converged, or when all have failed.\n    name: (Optional) Python str. The name prefixed to the ops created by this\n      function. If not supplied, the default name 'minimize' is used.\n\n  Returns:\n    optimizer_results: A namedtuple containing the following items:\n      converged: Scalar boolean tensor indicating whether the minimum was\n        found within tolerance.\n      failed:  Scalar boolean tensor indicating whether a line search\n        step failed to find a suitable step size satisfying Wolfe\n        conditions. In the absence of any constraints on the\n        number of objective evaluations permitted, this value will\n        be the complement of `converged`. However, if there is\n        a constraint and the search stopped due to available\n        evaluations being exhausted, both `failed` and `converged`\n        will be simultaneously False.\n      num_objective_evaluations: The total number of objective\n        evaluations performed.\n      position: A tensor containing the last argument value found\n        during the search. If the search converged, then\n        this value is the argmin of the objective function.\n      objective_value: A tensor containing the value of the objective\n        function at the `position`. If the search converged, then this is\n        the (local) minimum of the objective function.\n      objective_gradient: A tensor containing the gradient of the objective\n        function at the `position`. If the search converged the\n        max-norm of this tensor should be below the tolerance.\n      position_deltas: A tensor encoding information about the latest\n        changes in `position` during the algorithm execution.\n      gradient_deltas: A tensor encoding information about the latest\n        changes in `objective_gradient` during the algorithm execution.","confidence":0.76,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/lbfgs.py#L80-L260","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/lbfgs.py#L80-L260","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/lbfgs.py#L80-L260","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/lbfgs.py#L80-L260"},{"dataset":"csn-python","qid":"882","query":"Build a callable for one step of Kalman sampling recursion.\n\n  Args:\n    get_transition_matrix_for_timestep: callable taking a timestep\n      as an integer `Tensor` argument, and returning a `LinearOperator`\n      of shape `[latent_size, latent_size]`.\n    get_transition_noise_for_timestep: callable taking a timestep as\n      an integer `Tensor` argument, and returning a\n      `MultivariateNormalLinearOperator` of event shape\n      `[latent_size]`.\n    get_observation_matrix_for_timestep: callable taking a timestep\n      as an integer `Tensor` argument, and returning a `LinearOperator`\n      of shape `[observation_size, observation_size]`.\n    get_observation_noise_for_timestep: callable taking a timestep as\n      an integer `Tensor` argument, and returning a\n      `MultivariateNormalLinearOperator` of event shape\n      `[observation_size]`.\n    full_sample_and_batch_shape: Desired sample and batch shape of the\n      returned samples, concatenated in a single `Tensor`.\n    stream: `tfd.SeedStream` instance used to generate a\n      sequence of random seeds.\n    validate_args: if True, perform error checking at runtime.\n\n  Returns:\n    sample_step: a callable that samples the latent state and\n      observation at time `t`, given latent state at time `t-1`.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1622-L1685","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1622-L1685","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1622-L1685","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/linear_gaussian_ssm.py#L1622-L1685"},{"dataset":"csn-python","qid":"884","query":"Returns an iterable of strings that explain why this dependency wasn't met.\n\n        :param ti: the task instance to see if this dependency is met for\n        :type ti: airflow.models.TaskInstance\n        :param session: database session\n        :type session: sqlalchemy.orm.session.Session\n        :param dep_context: The context this dependency is being checked under that stores\n            state that can be used by this dependency.\n        :type dep_context: BaseDepContext","confidence":0.9,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L128-L142","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L128-L142","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L128-L142","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/ti_deps/deps/base_ti_dep.py#L128-L142"},{"dataset":"csn-python","qid":"888","query":"Delete a cluster and optionally create a snapshot\n\n        :param cluster_identifier: unique identifier of a cluster\n        :type cluster_identifier: str\n        :param skip_final_cluster_snapshot: determines cluster snapshot creation\n        :type skip_final_cluster_snapshot: bool\n        :param final_cluster_snapshot_identifier: name of final cluster snapshot\n        :type final_cluster_snapshot_identifier: str","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L46-L66","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L46-L66","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L46-L66","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/redshift_hook.py#L46-L66"},{"dataset":"csn-python","qid":"892","query":"Actual implementation of bisect given initial_args in a _BracketResult.","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L599-L643","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L599-L643","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L599-L643","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/internal/hager_zhang_lib.py#L599-L643"},{"dataset":"csn-python","qid":"894","query":"Returns whether the input matches the given determinant limit.\n\n  Args:\n    x: A floating-point `Tensor` of shape `[B1, ..., Bn, M, M]`.\n    det_bounds: A floating-point `Tensor` that must broadcast to shape\n      `[B1, ..., Bn]`, giving the desired lower bound on the\n      determinants in `x`.\n\n  Returns:\n    mask: A floating-point `Tensor` of shape [B1, ..., Bn].  Each\n      scalar is 1 if the corresponding matrix had determinant above\n      the corresponding bound, otherwise 0.","confidence":0.98,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L107-L130","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L107-L130","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L107-L130","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py#L107-L130"},{"dataset":"csn-python","qid":"895","query":"Returns a block diagonal rank 2 SparseTensor from a batch of SparseTensors.\n\n  Args:\n    sp_a: A rank 3 `SparseTensor` representing a batch of matrices.\n\n  Returns:\n    sp_block_diag_a: matrix-shaped, `float` `SparseTensor` with the same dtype\n    as `sparse_or_matrix`, of shape [B * M, B * N] where `sp_a` has shape\n    [B, M, N]. Each [M, N] batch of `sp_a` is lined up along the diagonal.","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/linalg.py#L874-L895","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/linalg.py#L874-L895","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/linalg.py#L874-L895","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/math/linalg.py#L874-L895"},{"dataset":"csn-python","qid":"897","query":"Load a pre-trained Bigdl model.\n\n        :param path: The path containing the pre-trained model.\n        :return: A pre-trained model.","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L68-L76","reverseDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L68-L76","forwardChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L68-L76","reverseChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L68-L76"},{"dataset":"csn-python","qid":"899","query":"Wrapper for tf.Print which supports lists and namedtuples for printing.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/hager_zhang.py#L666-L680","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/hager_zhang.py#L666-L680","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/hager_zhang.py#L666-L680","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/linesearch/hager_zhang.py#L666-L680"},{"dataset":"csn-python","qid":"900","query":"Returns the config of this layer.\n\n    NOTE: At the moment, this configuration can only be serialized if the\n    Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,\n    implements `get_config`) or one of the standard values:\n     - `Distribution.sample` (or `\"sample\"`)\n     - `Distribution.mean` (or `\"mean\"`)\n     - `Distribution.mode` (or `\"mode\"`)\n     - `Distribution.stddev` (or `\"stddev\"`)\n     - `Distribution.variance` (or `\"variance\"`)","confidence":0.35,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/distribution_layer.py#L732-L751","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/distribution_layer.py#L732-L751","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/distribution_layer.py#L732-L751","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/layers/distribution_layer.py#L732-L751"},{"dataset":"csn-python","qid":"901","query":"Set the running variance of the layer.\n        Only use this method for a BatchNormalization layer.\n        :param running_std: a Numpy array.","confidence":0.88,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L144-L152","reverseDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L144-L152","forwardChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L144-L152","reverseChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/layer.py#L144-L152"},{"dataset":"csn-python","qid":"902","query":"Kill (cancel) a Qubole command\n        :param ti: Task Instance of the dag, used to determine the Quboles command id\n        :return: response from Qubole","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L147-L162","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L147-L162","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L147-L162","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/qubole_hook.py#L147-L162"},{"dataset":"csn-python","qid":"909","query":"Creates a new database in CosmosDB.","confidence":0.99,"flipped":true,"forwardCorrect":true,"reverseCorrect":false,"forwardChoiceCorrect":true,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L142-L160","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_sql_hook.py#L205-L226","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L142-L160","reverseChoice":"none"},{"dataset":"csn-python","qid":"912","query":":param json_path: definition path which can be stored in a local file system, HDFS, S3, or any Hadoop-supported file system.\n        :return: BigDL Model","confidence":0.63,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L362-L368","reverseDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L362-L368","forwardChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L362-L368","reverseChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L362-L368"},{"dataset":"csn-python","qid":"917","query":"Calls `fn` and returns the gradients with respect to `fn`'s first output.\n\n  Args:\n    fn: A `TransitionOperator`.\n    args: Arguments to `fn`\n\n  Returns:\n    ret: First output of `fn`.\n    extra: Second output of `fn`.\n    grads: Gradients of `ret` with respect to `args`.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L140-L157","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L140-L157","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L140-L157","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/experimental/fun_mcmc/fun_mcmc_lib.py#L140-L157"},{"dataset":"csn-python","qid":"919","query":"Helper to `choose` which expand_dims `is_accepted` and applies tf.where.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/util.py#L74-L111","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/util.py#L74-L111","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/util.py#L74-L111","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/internal/util.py#L74-L111"},{"dataset":"csn-python","qid":"920","query":"Rotate the image by angle.\n\n\n    Args:\n        img (PIL Image): PIL Image to be rotated.\n        angle (float or int): In degrees degrees counter clockwise order.\n        resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):\n            An optional resampling filter. See `filters`_ for more information.\n            If omitted, or if the image has mode \"1\" or \"P\", it is set to ``PIL.Image.NEAREST``.\n        expand (bool, optional): Optional expansion flag.\n            If true, expands the output image to make it large enough to hold the entire rotated image.\n            If false or omitted, make the output image the same size as the input image.\n            Note that the expand flag assumes rotation around the center and no translation.\n        center (2-tuple, optional): Optional center of rotation.\n            Origin is the upper left corner.\n            Default is the center of the image.\n\n    .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L680-L705","reverseDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L680-L705","forwardChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L680-L705","reverseChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L680-L705"},{"dataset":"csn-python","qid":"922","query":"Compute exponentially weighted moving {mean,variance} of a streaming value.\n\n  The exponentially-weighting moving `mean_var` and `variance_var` are updated\n  by `value` according to the following recurrence:\n\n  ```python\n  variance_var = decay * (variance_var + (1-decay) * (value - mean_var)**2)\n  mean_var     = decay * mean_var + (1 - decay) * value\n  ```\n\n  Note: `mean_var` is updated *after* `variance_var`, i.e., `variance_var` uses\n  the lag-`1` mean.\n\n  For derivation justification, see [Finch (2009; Eq. 143)][1].\n\n  Unlike `assign_moving_mean_variance`, this function handles\n  variable creation.\n\n  Args:\n    value: `float`-like `Tensor`. Same shape as `mean_var` and `variance_var`.\n    decay: A `float`-like `Tensor`. The moving mean decay. Typically close to\n      `1.`, e.g., `0.999`.\n    name: Optional name of the returned operation.\n\n  Returns:\n    mean_var: `Variable` representing the `value`-updated exponentially weighted\n      moving mean.\n    variance_var: `Variable` representing the `value`-updated\n      exponentially weighted moving variance.\n\n  Raises:\n    TypeError: if `value_var` does not have float type `dtype`.\n    TypeError: if `value`, `decay` have different `base_dtype`.\n\n  #### References\n\n  [1]: Tony Finch. Incremental calculation of weighted mean and variance.\n       _Technical Report_, 2009.\n       http://people.ds.cam.ac.uk/fanf2/hermes/doc/antiforgery/stats.pdf","confidence":0.63,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/moving_stats.py#L186-L245","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/moving_stats.py#L186-L245","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/moving_stats.py#L186-L245","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/distributions/internal/moving_stats.py#L186-L245"},{"dataset":"csn-python","qid":"927","query":"Convert a ndarray of features and labels to Sample, which would be used in Java side.\n        :param features: an ndarray or a list of ndarrays\n        :param labels: an ndarray or a list of ndarrays or a scalar\n        :param bigdl_type: \"double\" or \"float\"\n\n        >>> import numpy as np\n        >>> from bigdl.util.common import callBigDlFunc\n        >>> from numpy.testing import assert_allclose\n        >>> np.random.seed(123)\n        >>> sample = Sample.from_ndarray(np.random.random((2,3)), np.random.random((2,3)))\n        >>> sample_back = callBigDlFunc(\"float\", \"testSample\", sample)\n        >>> assert_allclose(sample.features[0].to_ndarray(), sample_back.features[0].to_ndarray())\n        >>> assert_allclose(sample.label.to_ndarray(), sample_back.label.to_ndarray())\n        >>> expected_feature_storage = np.array(([[0.69646919, 0.28613934, 0.22685145], [0.55131477, 0.71946895, 0.42310646]]))\n        >>> expected_feature_shape = np.array([2, 3])\n        >>> expected_label_storage = np.array(([[0.98076421, 0.68482971, 0.48093191], [0.39211753, 0.343178, 0.72904968]]))\n        >>> expected_label_shape = np.array([2, 3])\n        >>> assert_allclose(sample.features[0].storage, expected_feature_storage, rtol=1e-6, atol=1e-6)\n        >>> assert_allclose(sample.features[0].shape, expected_feature_shape)\n        >>> assert_allclose(sample.labels[0].storage, expected_label_storage, rtol=1e-6, atol=1e-6)\n        >>> assert_allclose(sample.labels[0].shape, expected_label_shape)","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/util/common.py#L306-L345","reverseDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/util/common.py#L306-L345","forwardChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/util/common.py#L306-L345","reverseChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/util/common.py#L306-L345"},{"dataset":"csn-python","qid":"928","query":"Convenience function which statically broadcasts shape when possible.\n\n  Args:\n    shape1:  `1-D` integer `Tensor`.  Already converted to tensor!\n    shape2:  `1-D` integer `Tensor`.  Already converted to tensor!\n    name:  A string name to prepend to created ops.\n\n  Returns:\n    The broadcast shape, either as `TensorShape` (if broadcast can be done\n      statically), or as a `Tensor`.","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1323-L1365","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1323-L1365","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1323-L1365","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1323-L1365"},{"dataset":"csn-python","qid":"935","query":"Runs one iteration of NeuTra.\n\n    Args:\n      current_state: `Tensor` or Python `list` of `Tensor`s representing the\n        current state(s) of the Markov chain(s). The first `r` dimensions index\n        independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`.\n      previous_kernel_results: `collections.namedtuple` containing `Tensor`s\n        representing values from previous calls to this function (or from the\n        `bootstrap_results` function.)\n\n    Returns:\n      next_state: Tensor or Python list of `Tensor`s representing the state(s)\n        of the Markov chain(s) after taking exactly one step. Has same type and\n        shape as `current_state`.\n      kernel_results: `collections.namedtuple` of internal calculations used to\n        advance the chain.","confidence":0.7,"flipped":false,"forwardCorrect":false,"reverseCorrect":false,"forwardChoiceCorrect":false,"reverseChoiceCorrect":false,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/mcmc/transformed_kernel.py#L230-L273"},{"dataset":"csn-python","qid":"937","query":"Create a Python Model base on the given java value\n        :param jvalue: Java object create by Py4j\n        :return: A Python Model","confidence":0.96,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/keras/topology.py#L208-L216","reverseDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/keras/topology.py#L208-L216","forwardChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/keras/topology.py#L208-L216","reverseChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/nn/keras/topology.py#L208-L216"},{"dataset":"csn-python","qid":"94","query":"Check if a file exists on Azure Data Lake.\n\n        :param file_path: Path and name of the file.\n        :type file_path: str\n        :return: True if the file exists, False otherwise.\n        :rtype: bool","confidence":0.85,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L55-L68","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L55-L68","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L55-L68","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_data_lake_hook.py#L55-L68"},{"dataset":"csn-python","qid":"943","query":"Crop the given PIL Image and resize it to desired size.\n\n    Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.\n\n    Args:\n        img (PIL Image): Image to be cropped.\n        i (int): i in (i,j) i.e coordinates of the upper left corner\n        j (int): j in (i,j) i.e coordinates of the upper left corner\n        h (int): Height of the cropped image.\n        w (int): Width of the cropped image.\n        size (sequence or int): Desired output size. Same semantics as ``resize``.\n        interpolation (int, optional): Desired interpolation. Default is\n            ``PIL.Image.BILINEAR``.\n    Returns:\n        PIL Image: Cropped image.","confidence":0.95,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L372-L392","reverseDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L372-L392","forwardChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L372-L392","reverseChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L372-L392"},{"dataset":"csn-python","qid":"944","query":"Check if an S3 URL exists\n\n        :param s3url: S3 url\n        :type s3url: str\n        :rtype: bool","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L180-L201","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L180-L201","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L180-L201","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/sagemaker_hook.py#L180-L201"},{"dataset":"csn-python","qid":"945","query":"Checks if the executor agrees with the state of task instances\n        that are running\n\n        :param running: dict of key, task to verify","confidence":0.93,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1979-L2007","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1979-L2007","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1979-L2007","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/jobs.py#L1979-L2007"},{"dataset":"csn-python","qid":"956","query":"Build a loss function for variational inference in STS models.\n\n  Variational inference searches for the distribution within some family of\n  approximate posteriors that minimizes a divergence between the approximate\n  posterior `q(z)` and true posterior `p(z|observed_time_series)`. By converting\n  inference to optimization, it's generally much faster than sampling-based\n  inference algorithms such as HMC. The tradeoff is that the approximating\n  family rarely contains the true posterior, so it may miss important aspects of\n  posterior structure (in particular, dependence between variables) and should\n  not be blindly trusted. Results may vary; it's generally wise to compare to\n  HMC to evaluate whether inference quality is sufficient for your task at hand.\n\n  This method constructs a loss function for variational inference using the\n  Kullback-Liebler divergence `KL[q(z) || p(z|observed_time_series)]`, with an\n  approximating family given by independent Normal distributions transformed to\n  the appropriate parameter space for each parameter. Minimizing this loss (the\n  negative ELBO) maximizes a lower bound on the log model evidence `-log\n  p(observed_time_series)`. This is equivalent to the 'mean-field' method\n  implemented in [1]. and is a standard approach. The resulting posterior\n  approximations are unimodal; they will tend to underestimate posterior\n  uncertainty when the true posterior contains multiple modes (the `KL[q||p]`\n  divergence encourages choosing a single mode) or dependence between variables.\n\n  Args:\n    model: An instance of `StructuralTimeSeries` representing a\n      time-series model. This represents a joint distribution over\n      time-series and their parameters with batch shape `[b1, ..., bN]`.\n    observed_time_series: `float` `Tensor` of shape\n      `concat([sample_shape, model.batch_shape, [num_timesteps, 1]]) where\n      `sample_shape` corresponds to i.i.d. observations, and the trailing `[1]`\n      dimension may (optionally) be omitted if `num_timesteps > 1`. May\n      optionally be an instance of `tfp.sts.MaskedTimeSeries`, which includes\n      a mask `Tensor` to specify timesteps with missing observations.\n    init_batch_shape: Batch shape (Python `tuple`, `list`, or `int`) of initial\n      states to optimize in parallel.\n      Default value: `()`. (i.e., just run a single optimization).\n    seed: Python integer to seed the random number generator.\n    name: Python `str` name prefixed to ops created by this function.\n      Default value: `None` (i.e., 'build_factored_variational_loss').\n\n  Returns:\n    variational_loss: `float` `Tensor` of shape\n      `concat([init_batch_shape, model.batch_shape])`, encoding a stochastic\n      estimate of an upper bound on the negative model evidence `-log p(y)`.\n      Minimizing this loss performs variational inference; the gap between the\n      variational bound and the true (generally unknown) model evidence\n      corresponds to the divergence `KL[q||p]` between the approximate and true\n      posterior.\n    variational_distributions: `collections.OrderedDict` giving\n      the approximate posterior for each model parameter. The keys are\n      Python `str` parameter names in order, corresponding to\n      `[param.name for param in model.parameters]`. The values are\n      `tfd.Distribution` instances with batch shape\n      `concat([init_batch_shape, model.batch_shape])`; these will typically be\n      of the form `tfd.TransformedDistribution(tfd.Normal(...),\n      bijector=param.bijector)`.\n\n  #### Examples\n\n  Assume we've built a structural time-series model:\n\n  ```python\n    day_of_week = tfp.sts.Seasonal(\n        num_seasons=7,\n        observed_time_series=observed_time_series,\n        name='day_of_week')\n    local_linear_trend = tfp.sts.LocalLinearTrend(\n        observed_time_series=observed_time_series,\n        name='local_linear_trend')\n    model = tfp.sts.Sum(components=[day_of_week, local_linear_trend],\n                        observed_time_series=observed_time_series)\n  ```\n\n  To run variational inference, we simply construct the loss and optimize\n  it:\n\n  ```python\n    (variational_loss,\n     variational_distributions) = tfp.sts.build_factored_variational_loss(\n       model=model, observed_time_series=observed_time_series)\n\n    train_op = tf.train.AdamOptimizer(0.1).minimize(variational_loss)\n    with tf.Session() as sess:\n      sess.run(tf.global_variables_initializer())\n\n      for step in range(200):\n        _, loss_ = sess.run((train_op, variational_loss))\n\n        if step % 20 == 0:\n          print(\"step {} loss {}\".format(step, loss_))\n\n      posterior_samples_ = sess.run({\n        param_name: q.sample(50)\n        for param_name, q in variational_distributions.items()})\n  ```\n\n  As a more complex example, we might try to avoid local optima by optimizing\n  from multiple initializations in parallel, and selecting the result with the\n  lowest loss:\n\n  ```python\n    (variational_loss,\n     variational_distributions) = tfp.sts.build_factored_variational_loss(\n       model=model, observed_time_series=observed_time_series,\n       init_batch_shape=[10])\n\n    train_op = tf.train.AdamOptimizer(0.1).minimize(variational_loss)\n    with tf.Session() as sess:\n      sess.run(tf.global_variables_initializer())\n\n      for step in range(200):\n        _, loss_ = sess.run((train_op, variational_loss))\n\n        if step % 20 == 0:\n          print(\"step {} losses {}\".format(step, loss_))\n\n      # Draw multiple samples to reduce Monte Carlo error in the optimized\n      # variational bounds.\n      avg_loss = np.mean(\n        [sess.run(variational_loss) for _ in range(25)], axis=0)\n      best_posterior_idx = np.argmin(avg_loss, axis=0).astype(np.int32)\n  ```\n\n  #### References\n\n  [1]: Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and\n       David M. Blei. Automatic Differentiation Variational Inference. In\n       _Journal of Machine Learning Research_, 2017.\n       https://arxiv.org/abs/1603.00788","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/fitting.py#L92-L264","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/fitting.py#L92-L264","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/fitting.py#L92-L264","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/sts/fitting.py#L92-L264"},{"dataset":"csn-python","qid":"962","query":"get prediction rdd from ImageFrame","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/transform/vision/image.py#L290-L295","reverseDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/transform/vision/image.py#L290-L295","forwardChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/transform/vision/image.py#L290-L295","reverseChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/transform/vision/image.py#L290-L295"},{"dataset":"csn-python","qid":"963","query":"Returns a list of degree vectors, one for each input and hidden layer.\n\n  A unit with degree d can only receive input from units with degree < d. Output\n  units always have the same degree as their associated input unit.\n\n  Args:\n    input_size: Number of inputs.\n    hidden_units: list with the number of hidden units per layer. It does not\n      include the output layer. Each hidden unit size must be at least the size\n      of length (otherwise autoregressivity is not possible).\n    input_order: Order of degrees to the input units: 'random', 'left-to-right',\n      'right-to-left', or an array of an explicit order. For example,\n      'left-to-right' builds an autoregressive model\n      p(x) = p(x1) p(x2 | x1) ... p(xD | x<D).\n    hidden_degrees: Method for assigning degrees to the hidden units:\n      'equal', 'random'.  If 'equal', hidden units in each layer are allocated\n      equally (up to a remainder term) to each degree.  Default: 'equal'.\n\n  Raises:\n    ValueError: invalid input order.\n    ValueError: invalid hidden degrees.","confidence":0.94,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L903-L954","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L903-L954","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L903-L954","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L903-L954"},{"dataset":"csn-python","qid":"968","query":"r\"\"\"Creates a (batch of) triangular matrix from a vector of inputs.\n\n  Created matrix can be lower- or upper-triangular. (It is more efficient to\n  create the matrix as upper or lower, rather than transpose.)\n\n  Triangular matrix elements are filled in a clockwise spiral. See example,\n  below.\n\n  If `x.shape` is `[b1, b2, ..., bB, d]` then the output shape is\n  `[b1, b2, ..., bB, n, n]` where `n` is such that `d = n(n+1)/2`, i.e.,\n  `n = int(np.sqrt(0.25 + 2. * m) - 0.5)`.\n\n  Example:\n\n  ```python\n  fill_triangular([1, 2, 3, 4, 5, 6])\n  # ==> [[4, 0, 0],\n  #      [6, 5, 0],\n  #      [3, 2, 1]]\n\n  fill_triangular([1, 2, 3, 4, 5, 6], upper=True)\n  # ==> [[1, 2, 3],\n  #      [0, 5, 6],\n  #      [0, 0, 4]]\n  ```\n\n  The key trick is to create an upper triangular matrix by concatenating `x`\n  and a tail of itself, then reshaping.\n\n  Suppose that we are filling the upper triangle of an `n`-by-`n` matrix `M`\n  from a vector `x`. The matrix `M` contains n**2 entries total. The vector `x`\n  contains `n * (n+1) / 2` entries. For concreteness, we'll consider `n = 5`\n  (so `x` has `15` entries and `M` has `25`). We'll concatenate `x` and `x` with\n  the first (`n = 5`) elements removed and reversed:\n\n  ```python\n  x = np.arange(15) + 1\n  xc = np.concatenate([x, x[5:][::-1]])\n  # ==> array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 14, 13,\n  #            12, 11, 10, 9, 8, 7, 6])\n\n  # (We add one to the arange result to disambiguate the zeros below the\n  # diagonal of our upper-triangular matrix from the first entry in `x`.)\n\n  # Now, when reshapedlay this out as a matrix:\n  y = np.reshape(xc, [5, 5])\n  # ==> array([[ 1,  2,  3,  4,  5],\n  #            [ 6,  7,  8,  9, 10],\n  #            [11, 12, 13, 14, 15],\n  #            [15, 14, 13, 12, 11],\n  #            [10,  9,  8,  7,  6]])\n\n  # Finally, zero the elements below the diagonal:\n  y = np.triu(y, k=0)\n  # ==> array([[ 1,  2,  3,  4,  5],\n  #            [ 0,  7,  8,  9, 10],\n  #            [ 0,  0, 13, 14, 15],\n  #            [ 0,  0,  0, 12, 11],\n  #            [ 0,  0,  0,  0,  6]])\n  ```\n\n  From this example we see that the resuting matrix is upper-triangular, and\n  contains all the entries of x, as desired. The rest is details:\n  - If `n` is even, `x` doesn't exactly fill an even number of rows (it fills\n    `n / 2` rows and half of an additional row), but the whole scheme still\n    works.\n  - If we want a lower triangular matrix instead of an upper triangular,\n    we remove the first `n` elements from `x` rather than from the reversed\n    `x`.\n\n  For additional comparisons, a pure numpy version of this function can be found\n  in `distribution_util_test.py`, function `_fill_triangular`.\n\n  Args:\n    x: `Tensor` representing lower (or upper) triangular elements.\n    upper: Python `bool` representing whether output matrix should be upper\n      triangular (`True`) or lower triangular (`False`, default).\n    name: Python `str`. The name to give this op.\n\n  Returns:\n    tril: `Tensor` with lower (or upper) triangular elements filled from `x`.\n\n  Raises:\n    ValueError: if `x` cannot be mapped to a triangular matrix.","confidence":0.89,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1415-L1561","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1415-L1561","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1415-L1561","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/distribution_util.py#L1415-L1561"},{"dataset":"csn-python","qid":"97","query":"Deletes an existing collection in the CosmosDB database.","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L171-L179","reverseDid":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L171-L179","forwardChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L171-L179","reverseChoice":"https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_cosmos_hook.py#L171-L179"},{"dataset":"csn-python","qid":"972","query":"See tfkl.Layer.call.","confidence":0.41,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":false,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L854-L863","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L854-L863","forwardChoice":"none","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/bijectors/masked_autoregressive.py#L854-L863"},{"dataset":"csn-python","qid":"973","query":"Transpose a possibly batched matrix.\n\n  Args:\n    mat: A `tf.Tensor` of shape `[..., n, m]`.\n\n  Returns:\n    A tensor of shape `[..., m, n]` with matching batch dimensions.","confidence":0.97,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/bfgs.py#L494-L506","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/bfgs.py#L494-L506","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/bfgs.py#L494-L506","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/optimizer/bfgs.py#L494-L506"},{"dataset":"csn-python","qid":"985","query":"Returns whether this is a complex floating point type.","confidence":0.76,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L90-L95","reverseDid":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L90-L95","forwardChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L90-L95","reverseChoice":"https://github.com/tensorflow/probability/blob/e87fe34111d68c35db0f9eeb4935f1ece9e1a8f5/tensorflow_probability/python/internal/dtype_util.py#L90-L95"},{"dataset":"csn-python","qid":"987","query":"Horizontally flip the given PIL Image.\n\n    Args:\n        img (PIL Image): Image to be flipped.\n\n    Returns:\n        PIL Image:  Horizontall flipped image.","confidence":1.0,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L395-L407","reverseDid":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L395-L407","forwardChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L395-L407","reverseChoice":"https://github.com/pytorch/vision/blob/3afcf3cd49661c466c75ea536b0b2a7ff57f9a05/torchvision/transforms/functional.py#L395-L407"},{"dataset":"csn-python","qid":"989","query":"Computes additive self attention for time series of vectors (with batch dimension)\n        the formula: score(h_i, h_j) = <v, tanh(W_1 h_i + W_2 h_j)>\n        v is a learnable vector of n_hidden dimensionality,\n        W_1 and W_2 are learnable [n_hidden, n_input_features] matrices\n\n    Args:\n        units: tf tensor with dimensionality [batch_size, time_steps, n_input_features]\n        n_hidden: number of units in hidden representation of similarity measure\n        n_output_features: number of features in output dense layer\n        activation: activation at the output\n\n    Returns:\n        output: self attended tensor with dimensionality [batch_size, time_steps, n_output_features]","confidence":0.9,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/deepmipt/DeepPavlov/blob/f3e4a69a3764d25d2f5bad4f1f1aebc872b00f9c/deeppavlov/core/layers/tf_layers.py#L447-L472","reverseDid":"https://github.com/deepmipt/DeepPavlov/blob/f3e4a69a3764d25d2f5bad4f1f1aebc872b00f9c/deeppavlov/core/layers/tf_layers.py#L447-L472","forwardChoice":"https://github.com/deepmipt/DeepPavlov/blob/f3e4a69a3764d25d2f5bad4f1f1aebc872b00f9c/deeppavlov/core/layers/tf_layers.py#L447-L472","reverseChoice":"https://github.com/deepmipt/DeepPavlov/blob/f3e4a69a3764d25d2f5bad4f1f1aebc872b00f9c/deeppavlov/core/layers/tf_layers.py#L447-L472"},{"dataset":"csn-python","qid":"998","query":"Convert kmodel's weights to bigdl format.\n        We are supposing the order is the same as the execution order.\n        :param kmodel: keras model\n        :return: list of ndarray","confidence":0.99,"flipped":false,"forwardCorrect":true,"reverseCorrect":true,"forwardChoiceCorrect":true,"reverseChoiceCorrect":true,"forwardDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L138-L152","reverseDid":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L138-L152","forwardChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L138-L152","reverseChoice":"https://github.com/intel-analytics/BigDL/blob/e9c19788285986ab789a2e2998f9a85d7524779f/pyspark/bigdl/keras/converter.py#L138-L152"}]}