{"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":"f14d8022-4612-54a1-be53-52b013431048","task_key":"default--test--945","task_revision_id":"1","upstream_id":"945","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\\n\\nC = torch.LongTensor([[1, 3], [4, 6]])\\n# 1 3\\n# 4 6\\nLogical indexing on the columns only:\\n\\nA_log = torch.ByteTensor([1, 0, 1]) # the logical index\\nB = torch.LongTensor([[1, 2, 3], [4, 5, 6]])\\nC = B[:, A_log] # Throws error\\nIf the vectors are the same size, logical indexing works:\\n\\nB_truncated = torch.LongTensor([1, 2, 3])\\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()\\ndef solve(A_log, B):\\n    # return the solution in this function\\n    # C = solve(A_log, B)\\n    ### BEGIN SOLUTION\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}