{"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":"ee9ed4a2-5e2d-5bde-ae1f-1531513f1fe2","task_key":"default--test--988","task_revision_id":"1","upstream_id":"988","short_description":"This question may not be clear, so please ask for clarification in the comments…","config":"default","split":"test","body":"{\"library\":\"Pytorch\",\"prompt\":\"Problem:\\n\\nThis question may not be clear, so please ask for clarification in the comments and I will expand.\\n\\nI have the following tensors of the following shape:\\n\\nmask.size() == torch.Size([1, 400])\\nclean_input_spectrogram.size() == torch.Size([1, 400, 161])\\noutput.size() == torch.Size([1, 400, 161])\\nmask is comprised only of 0 and 1. Since it's a mask, I want to set the elements of output equal to clean_input_spectrogram where that relevant mask value is 1.\\n\\nHow would I do that?\\n\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nmask, clean_input_spectrogram, output= load_data()\\n</code>\\noutput = ... # 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":[]}