{"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":"7d9e812b-ef97-59f1-8423-e1924d51033e","task_key":"default--v0~2e1~2e0~5fhf--7d9e812b-ef97-59f1-8423-e1924d51033e","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 7d9e812b-ef97-59f1-8423-e1924d51033e","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import matplotlib\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\ndef task_func(mu, sigma, sample_size, seed=0):\\n\",\"complete_prompt\":\"import matplotlib\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\n\\n\\ndef task_func(mu, sigma, sample_size, seed=0):\\n    \\\"\\\"\\\"\\n    Create a Gaussian kernel density estimate diagram of a normal distribution with a given mean and a \\n    standard deviation using a random sample of a size determined by the sample_size parameter. The density \\n    diagram is plotted using default settings in a deterministic matplotlib plot. Return the axes object.\\n    \\n    Parameters:\\n    mu (float): The mean of the normal distribution.\\n    sigma (float): The standard deviation of the normal distribution.\\n    sample_size (int): The size of the sample to generate. Must be a positive integer.\\n    seed (int, Optional): The seed to be used for the random number generator. Default is 0.\\n    \\n    Returns:\\n    matplotlib.axes._axes.Axes: Axes object containing the plot of the normal distribution.\\n    \\n    Requirements:\\n    - numpy\\n    - matplotlib\\n    - scipy.stats\\n    \\n    Example:\\n    >>> ax = task_func(0, 1, 1000)\\n    >>> type(ax) # The result should be a matplotlib.axes._axes.Axes object\\n    <class 'matplotlib.axes._axes.Axes'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create a Gaussian kernel density estimate diagram of a normal distribution with a given mean and a standard deviation using a random sample of a size determined by the sample_size parameter. The density diagram is plotted using default settings in a deterministic matplotlib plot. Return the axes object.\\nThe function should output with:\\n    matplotlib.axes._axes.Axes: Axes object containing the plot of the normal distribution.\\nYou should write self-contained code starting with:\\n```\\nimport matplotlib\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\ndef task_func(mu, sigma, sample_size, seed=0):\\n```\",\"libs\":\"['numpy', 'matplotlib', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}