{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.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":"d8712f0c-afc4-5a64-8188-1d22819aefe3","task_key":"default--v0~2e1~2e0~5fhf--d8712f0c-afc4-5a64-8188-1d22819aefe3","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf d8712f0c-afc4-5a64-8188-1d22819aefe3","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(P, T):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(P, T):\\n    \\\"\\\"\\\"\\n    Calculate the product of a matrix \\\"P\\\" and a 3D tensor \\\"T\\\" with numpy and then visualize the\\n    result in 3D with matplotlib. The product of the matrix and tensor is based on the Einstein summation.\\n    \\n    Note:\\n    This function only accepts numpy matrices/arrays.\\n\\n    Parameters:\\n    P (numpy.ndarray): The input matrix with shape (N, 3), where N is the number of rows.\\n    T (numpy.ndarray): The input tensor with shape (3, 3, 3).\\n\\n    Returns:\\n    tuple:\\n        - result (numpy.ndarray): The product of matrix P and tensor T with shape (N, 3).\\n        - ax (mpl_toolkits.mplot3d.axes3d.Axes3D): The 3D visualization of the result.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> P = np.array([[6, 2, 7], [1, 1, 8], [8, 7, 1]])\\n    >>> T = np.random.rand(3, 3, 3)\\n    >>> result, ax = task_func(P, T)\\n    >>> type(result)\\n    <class 'numpy.ndarray'>\\n    >>> type(ax)\\n    <class 'mpl_toolkits.mplot3d.axes3d.Axes3D'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculate the product of a matrix \\\"P\\\" and a 3D tensor \\\"T\\\" with numpy and then visualize the result in 3D with matplotlib. The product of the matrix and tensor is based on the Einstein summation.\\nNote that: This function only accepts numpy matrices/arrays.\\nThe function should output with:\\n    tuple:\\n    result (numpy.ndarray): The product of matrix P and tensor T with shape (N, 3).\\n    ax (mpl_toolkits.mplot3d.axes3d.Axes3D): The 3D visualization of the result.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(P, T):\\n```\",\"libs\":\"['numpy', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}