Repository: scipy/scipy
File: scipy/stats/_distribution_infrastructure.py (Python)

Code before:
            values, filling in values from other parameterizations using values from
            the supplied parameterization.

        support : dict or tuple
            A dictionary describing the support of the distribution or a tuple
            describing the endpoints of the support. This behaves identically to
            the values of the parameters dict described above, except that the key
            ``typical`` is ignored.

        The class **must** also define a ``pdf`` method and **may** define methods
        ``logentropy``, ``entropy``, ``median``, ``mode``, ``logpdf``,
        ``logcdf``, ``cdf``, ``logccdf``, ``ccdf``,
        ``ilogcdf``, ``icdf``, ``ilogccdf``, ``iccdf``,
        ``moment``, and ``sample``.
        If defined, these methods must accept the parameters of the distribution as
        keyword arguments and also accept any positional-only arguments accepted by
        the corresponding method of `ContinuousDistribution`. When multiple
        parameterizations are defined, these methods must accept all parameters from
        all parameterizations. The ``moment`` method must accept the ``order`` and
        ``kind`` arguments by position or keyword, but may return ``None`` if a
        formula is not available for the arguments; in this case, the infrastructure
        will fall back to a default implementation. The ``sample`` method must accept
        ``shape`` by position or keyword, but contrary to the public method of the
        same name, the argument it receives will be the *full* shape of the output
        array - that is, the shape passed to the public method prepended to the
        broadcasted shape of random variable parameters.

    Returns
    -------
    CustomDistribution : `ContinuousDistribution`
        A subclass of `ContinuousDistribution` corresponding with `dist`. The
        initializer requires all shape parameters to be passed as keyword arguments
        (using the same names as the instance of `rv_continuous`).

    Notes
    -----
    The documentation of `ContinuousDistribution` is not rendered. See below for
    an example of how to instantiate the class (i.e. pass all shape parameters of
    `dist` to the initializer as keyword arguments). Documentation of all methods
    is identical to that of `scipy.stats.Normal`. Use ``help`` on the returned
    class or its methods for more information.

    Examples
    --------
    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> from scipy import stats
    >>> from scipy import special

    Create a `ContinuousDistribution` from `scipy.stats.loguniform`.


Review comment (suggestion):
Just want to see if anything else changed. GH is not showing the diff very nicely.
```suggestion
        the corresponding method of `ContinuousDistribution`. 
        When multiple parameterizations are defined, these methods must accept
        all parameters from all parameterizations. The ``moment`` method
        must accept the ``order`` and ``kind`` arguments by position or keyword, but
        may return ``None`` if a formula is not available for the arguments; in this
        case, the infrastructure will fall back to a default implementation. The
        ``sample`` method must accept ``shape`` by position or keyword, but contrary
        to the public method of the same name, the argument it receives will be the
        *full* shape of the output array - that is, the shape passed to the public
        method prepended to the broadcasted shape of random variable parameters.
```

Code after:
            values, filling in values from other parameterizations using values from
            the supplied parameterization. See example.

        support : dict or tuple
            A dictionary describing the support of the distribution or a tuple
            describing the endpoints of the support. This behaves identically to
            the values of the parameters dict described above, except that the key
            ``typical`` is ignored.

        The class **must** also define a ``pdf`` method and **may** define methods
        ``logentropy``, ``entropy``, ``median``, ``mode``, ``logpdf``,
        ``logcdf``, ``cdf``, ``logccdf``, ``ccdf``,
        ``ilogcdf``, ``icdf``, ``ilogccdf``, ``iccdf``,
        ``moment``, and ``sample``.
        If defined, these methods must accept the parameters of the distribution as
        keyword arguments and also accept any positional-only arguments accepted by
        the corresponding method of `ContinuousDistribution`. 
        When multiple parameterizations are defined, these methods must accept
        all parameters from all parameterizations. The ``moment`` method
        must accept the ``order`` and ``kind`` arguments by position or keyword, but
        may return ``None`` if a formula is not available for the arguments; in this
        case, the infrastructure will fall back to a default implementation. The
        ``sample`` method must accept ``shape`` by position or keyword, but contrary
        to the public method of the same name, the argument it receives will be the
        *full* shape of the output array - that is, the shape passed to the public
        method prepended to the broadcasted shape of random variable parameters.

    Returns
    -------
    CustomDistribution : `ContinuousDistribution`
        A subclass of `ContinuousDistribution` corresponding with `dist`. The
        initializer requires all shape parameters to be passed as keyword arguments
        (using the same names as the instance of `rv_continuous`).

    Notes
    -----
    The documentation of `ContinuousDistribution` is not rendered. See below for
    an example of how to instantiate the class (i.e. pass all shape parameters of
    `dist` to the initializer as keyword arguments). Documentation of all methods
    is identical to that of `scipy.stats.Normal`. Use ``help`` on the returned
    class or its methods for more information.

    Examples
    --------
    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> from scipy import stats
    >>> from scipy import special

    Create a `ContinuousDistribution` from `scipy.stats.loguniform`.