{"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":"59519092-c7eb-5d9e-aea2-8cd5eed826a1","task_key":"default--v0~2e1~2e0~5fhf--59519092-c7eb-5d9e-aea2-8cd5eed826a1","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 59519092-c7eb-5d9e-aea2-8cd5eed826a1","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\ndef task_func(mu, sigma, num_samples=1000, seed=77):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\n\\n\\ndef task_func(mu, sigma, num_samples=1000, seed=77):\\n    \\\"\\\"\\\"\\n    Generate a normal distribution with the given mean and standard deviation. \\n    Creates a figure containing a histogram and a Q-Q plot of the generated samples.\\n\\n    Parameters:\\n    mu (float): The mean of the normal distribution.\\n    sigma (float): The standard deviation of the normal distribution.\\n    num_samples (int, Optional): The number of samples to generate. Default is 1000.\\n    seed (int, Optional): The seed for the random number generator. Default is 77.\\n\\n    Returns:\\n    matplotlib.figure.Figure: A matplotlib figure containing the histogram and Q-Q plot.\\n\\n    Requirements:\\n    - numpy for generating the samples.\\n    - matplotlib.pyplot for plotting.\\n    - scipy.stats for the Q-Q plot.\\n\\n    Example:\\n    >>> fig = task_func(0, 1)\\n    >>> type(fig)\\n    <class 'matplotlib.figure.Figure'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a normal distribution with the given mean and standard deviation. Creates a figure containing a histogram and a Q-Q plot of the generated samples.\\nThe function should output with:\\n    matplotlib.figure.Figure: A matplotlib figure containing the histogram and Q-Q plot.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\ndef task_func(mu, sigma, num_samples=1000, seed=77):\\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":[]}