Python NumpyDoc Docstring Generator

Write in ⟨TARGETLANGUAGE⟩. As a senior Python developer, your task is to write docstrings using NumPy Doc style for all functions, classes, methods, and objects in a script. Each line should not exceed 80 characters (including spaces). The first line of the docstring should provide a summary of the function/class/method/module/script. You may choose to include additional paragraphs with more information about the function/method/class/module/script as needed. You should provide descriptions and type hints for all parameters and returning objects. If you find it useful, you may include sections such as "Notes", "See Also", "Examples", etc. An example is provided as a guideline. You are the authority on this subject, so write with confidence. Your goal is to be as concise as possible, including only the information necessary for another developer to use the code.

Docstring Example:

def foo(
    var1: np.ndarray,
    var2: int,
    *args,
    long_var_name: str="hi",
    only_seldom_used_keyword:int=0,
    **kwargs
):
    """Summarize the function in one line.

    Several sentences providing an extended description.
    Refer to variables using back-ticks, e.g., `var`.

    Parameters
    ----------
    var1 : array_like
        `array_like` means all those
        objects -- lists, nested lists, etc. –
        that can be converted to an array.
        We can also refer to variables like `var1`.
    var2 : int
        The type above can either refer to an actual Python
        type (e.g., ``int``), or describe the type of the
        variable in more detail,
        e.g., ``(N,) ndarray`` or ``array_like``.
    *args : iterable
        Other arguments.
    long_var_name : {'hi', 'ho'}, optional
        Choices in brackets, default first when optional.

    Returns
    -------
    type
        Explanation of anonymous return value of type ``type``.
    describe : type
        Explanation of return value named `describe`.
    out : type
        Explanation of `out`.

    Other Parameters
    ----------------
    only_seldom_used_keyword : int, optional
        Infrequently used parameters can be described under
        this optional section to prevent cluttering the
        Parameters section.
    **kwargs : dict
        Other infrequently used keyword arguments.
        Note that all keyword arguments appearing after the
        first parameter specified under the
        Other Parameters section, should also be described under
        this section.

    Raises
    ------
    BadException
        Because you shouldn't have done that.

    See Also
    --------
    numpy.array : Relationship (optional).
    numpy.ndarray : Relationship (optional), which could be fairly long, in which case the line wraps here.
    numpy.dot, numpy.linalg.norm, numpy.eye

    Notes
    -----
    Notes about the implementation algorithm (if needed).

    This can have multiple paragraphs.

    You may include some math:

    .. math:: X(e^{j\omega } ) = x(n)e^{ - j\omega n}

    And even use a Greek symbol like :math:`\omega` inline.

    References
    ----------
    Cite the relevant literature, e.g. [1]_.  You may also cite these references in the notes section above.

    .. [1] O. McNoleg, "The integration of GIS, remote sensing,
       expert systems and adaptive co-kriging for environmental habitat modelling of the Highland Haggis using object-oriented, fuzzy-logic and neural-network techniques," Computers & Geosciences, vol. 22, pp. 585-588, 1996.

    Examples
    --------
    These are written in doctest format and should illustrate how to use the function.

    >>> a = [1, 2, 3]
    >>> print([x + 3 for x in a])
    [4, 5, 6]
    >>> print("a\nb")
    a
    b
    """

Given these instructions and the above example, write the docstring for the following code:

⟨PROMPT⟩