{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"ds-1000","formal_name":"DS-1000","introduction":"DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve them.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://ds1000-code-gen.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"051847f6-0549-5b82-8cec-7954eae3b7f5","task_key":"default--test--975","task_revision_id":"1","upstream_id":"975","short_description":"I have a logistic regression model using Pytorch, where my input is…","config":"default","split":"test","body":"{\"library\":\"Pytorch\",\"prompt\":\"Problem:\\n\\nI have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.\\n\\nI'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1 or 2).\\n\\nHowever, I must return a n x 1 tensor, so I need to somehow pick the highest probability for each input and create a tensor indicating which class had the highest probability. How can I achieve this using Pytorch?\\n\\nTo illustrate, my Softmax outputs this:\\n\\n[[0.7, 0.2, 0.1],\\n [0.2, 0.6, 0.2],\\n [0.1, 0.1, 0.8]]\\nAnd I must return this:\\n\\n[[0],\\n [1],\\n [2]]\\n\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nsoftmax_output = load_data()\\n</code>\\ny = ... # put solution in this variable\\nBEGIN SOLUTION\\n<code>\\n\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}