{"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":"f075f24c-98e0-5b6a-ac2e-961f40eb92df","task_key":"default--test--946","task_revision_id":"1","upstream_id":"946","short_description":"I want to use a logical index to slice a torch tensor. Which means, I want to…","config":"default","split":"test","body":"{\"library\":\"Pytorch\",\"prompt\":\"Problem:\\n\\nI want to use a logical index to slice a torch tensor. Which means, I want to select the columns that get a '0' in the logical index.\\nI tried but got some errors:\\nTypeError: indexing a tensor with an object of type ByteTensor. The only supported types are integers, slices, numpy scalars and torch.LongTensor or torch.ByteTensor as the only argument.\\n\\nDesired Output like\\nimport torch\\nC = torch.LongTensor([[999, 777], [9999, 7777]])\\n\\nAnd Logical indexing on the columns:\\nA_log = torch.ByteTensor([0, 0, 1]) # the logical index\\nB = torch.LongTensor([[999, 777, 114514], [9999, 7777, 1919810]])\\nC = B[:, A_log] # Throws error\\n\\nHowever, if the vectors are of the same size, logical indexing works:\\nB_truncated = torch.LongTensor([114514, 1919, 810])\\nC = B_truncated[A_log]\\n\\nI'm confused about this, can you help me about this?\\n\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nA_log, B = load_data()\\n</code>\\nC = ... # 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":[]}