{"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":"2579393c-5e70-5774-8768-d65a85e9bf67","task_key":"default--test--943","task_revision_id":"1","upstream_id":"943","short_description":"I'm trying to slice a PyTorch tensor using a logical index on the columns. I…","config":"default","split":"test","body":"{\"library\":\"Pytorch\",\"prompt\":\"Problem:\\n\\nI'm trying to slice a PyTorch tensor using a logical index on the columns. I want the columns that correspond to a 1 value in the index vector. Both slicing and logical indexing are possible, but are they possible together? If so, how? My attempt keeps throwing the unhelpful error\\n\\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\\nMCVE\\nDesired Output\\n\\nimport torch\\nC = torch.LongTensor([[999, 777], [9999, 7777]])\\nLogical indexing on the columns only:\\n\\nA_log = torch.ByteTensor([1, 1, 0]) # the logical index\\nB = torch.LongTensor([[999, 777, 114514], [9999, 7777, 1919810]])\\nC = B[:, A_log] # Throws error\\nIf the vectors are the same size, logical indexing works:\\n\\nB_truncated = torch.LongTensor([114514, 1919, 810])\\nC = B_truncated[A_log]\\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":[]}