{"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":"a3d32085-d2de-5f3a-a20b-2e3772223922","task_key":"default--test--999","task_revision_id":"1","upstream_id":"999","short_description":"I have batch data and want to dot() to the data. W is trainable parameters. How…","config":"default","split":"test","body":"{\"library\":\"Pytorch\",\"prompt\":\"Problem:\\n\\nI have batch data and want to dot() to the data. W is trainable parameters. How to dot between batch data and weights?\\nHere is my code below, how to fix it?\\n\\nhid_dim = 32\\ndata = torch.randn(10, 2, 3, hid_dim)\\ndata = data.view(10, 2*3, hid_dim)\\nW = torch.randn(hid_dim) # assume trainable parameters via nn.Parameter\\nresult = torch.bmm(data, W).squeeze() # error, want (N, 6)\\nresult = result.view(10, 2, 3)\\n\\n\\nA:\\n\\ncorrected, runnable code\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nhid_dim = 32\\ndata = torch.randn(10, 2, 3, hid_dim)\\ndata = data.view(10, 2 * 3, hid_dim)\\nW = torch.randn(hid_dim)\\n</code>\\nresult = ... # 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":[]}