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        The axis to which `sl` is applied. All other dimensions are left
        "unsliced".

    Returns
    -------
    sl : tuple of slices
        A tuple with slices matching `shape` in length.

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    Parameters
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    array : ndarray
        The array with the region of interest.
    original_area_slice : tuple of slices
        Denotes the area with original values of the unpadded array.
    axis : int
        The currently padded dimension assuming that `axis` is padded before
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    fill_value : scalar, optional
        If provided the padded area is filled with this value, otherwise
        the pad area left undefined.

    Returns
    -------
    padded : ndarray
        The padded array with the same dtype as`array`. Its order will default
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        Array with the pad area which is modified inplace.
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    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
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    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.

    Returns
    -------
    left_edge, right_edge : ndarray
        Edge values of the valid area in `padded` in the given dimension. Its
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        Dimension in which the ramps are constructed.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.
    end_value_pair : (scalar, scalar)
        End values for the linear ramps which form the edge of the fully padded
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        Dimension in which the statistic is calculated.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.
    length_pair : 2-element sequence of None or int
        Gives the number of values in valid area from each side that is
        taken into account when calculating the statistic. If None the entire
        valid area in `padded` is considered.
    stat_func : function
        Function to compute statistic. The expected signature is
        ``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``.

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        Axis along which to pad `arr`.
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        Pair of widths that mark the pad area on both sides in the given
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    method : str
        Controls method of reflection; options are 'even' or 'odd'.
    include_edge : bool
        If true, edge value is included in reflection, otherwise the edge
        value forms the symmetric axis to the reflection.

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        Pair of widths that mark the pad area on both sides in the given
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        The object to broadcast to the shape (`ndim`, 2).
    ndim : int
        Number of pairs the broadcasted `x` will have.
    as_index : bool, optional
        If `x` is not None, try to round each element of `x` to an integer
        (dtype `np.intp`) and ensure every element is positive.

    Returns
    -------
    pairs : nested iterables, shape (`ndim`, 2)
        The broadcasted version of `x`.

    Raises
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    Pad an array.

    Parameters
    ----------
    array : array_like of rank N
        The array to pad.
    pad_width : {sequence, array_like, int}
        Number of values padded to the edges of each axis.
        ((before_1, after_1), ... (before_N, after_N)) unique pad widths
        for each axis.
        ((before, after),) yields same before and after pad for each axis.
        (pad,) or int is a shortcut for before = after = pad width for all
        axes.
    mode : str or function, optional
        One of the following string values or a user supplied function.

        'constant' (default)
            Pads with a constant value.
        'edge'
            Pads with the edge values of array.
        'linear_ramp'
            Pads with the linear ramp between end_value and the
            array edge value.
        'maximum'
            Pads with the maximum value of all or part of the
            vector along each axis.
        'mean'
            Pads with the mean value of all or part of the
            vector along each axis.
        'median'
            Pads with the median value of all or part of the
            vector along each axis.
        'minimum'
            Pads with the minimum value of all or part of the
            vector along each axis.
        'reflect'
            Pads with the reflection of the vector mirrored on
            the first and last values of the vector along each
            axis.
        'symmetric'
            Pads with the reflection of the vector mirrored
            along the edge of the array.
        'wrap'
            Pads with the wrap of the vector along the axis.
            The first values are used to pad the end and the
            end values are used to pad the beginning.
        'empty'
            Pads with undefined values.

            .. versionadded:: 1.17

        <function>
            Padding function, see Notes.
    stat_length : sequence or int, optional
        Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
        values at edge of each axis used to calculate the statistic value.

        ((before_1, after_1), ... (before_N, after_N)) unique statistic
        lengths for each axis.

        ((before, after),) yields same before and after statistic lengths
        for each axis.

        (stat_length,) or int is a shortcut for before = after = statistic
        length for all axes.

        Default is ``None``, to use the entire axis.
    constant_values : sequence or scalar, optional
        Used in 'constant'.  The values to set the padded values for each
        axis.

        ``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
        for each axis.

        ``((before, after),)`` yields same before and after constants for each
        axis.

        ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
        all axes.

        Default is 0.
    end_values : sequence or scalar, optional
        Used in 'linear_ramp'.  The values used for the ending value of the
        linear_ramp and that will form the edge of the padded array.

        ``((before_1, after_1), ... (before_N, after_N))`` unique end values
        for each axis.

        ``((before, after),)`` yields same before and after end values for each
        axis.

        ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
        all axes.

        Default is 0.
    reflect_type : {'even', 'odd'}, optional
        Used in 'reflect', and 'symmetric'.  The 'even' style is the
        default with an unaltered reflection around the edge value.  For
        the 'odd' style, the extended part of the array is created by
        subtracting the reflected values from two times the edge value.

    Returns
    -------
    pad : ndarray
        Padded array of rank equal to `array` with shape increased
        according to `pad_width`.

    Notes
    -----
    .. versionadded:: 1.7.0

    For an array with rank greater than 1, some of the padding of later
    axes is calculated from padding of previous axes.  This is easiest to
    think about with a rank 2 array where the corners of the padded array
    are calculated by using padded values from the first axis.

    The padding function, if used, should modify a rank 1 array in-place. It
    has the following signature::

        padding_func(vector, iaxis_pad_width, iaxis, kwargs)

    where

        vector : ndarray
            A rank 1 array already padded with zeros.  Padded values are
            vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
        iaxis_pad_width : tuple
            A 2-tuple of ints, iaxis_pad_width[0] represents the number of
            values padded at the beginning of vector where
            iaxis_pad_width[1] represents the number of values padded at
            the end of vector.
        iaxis : int
            The axis currently being calculated.
        kwargs : dict
            Any keyword arguments the function requires.

    Examples
    --------
    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
    array([4, 4, 1, ..., 6, 6, 6])

    >>> np.pad(a, (2, 3), 'edge')
    array([1, 1, 1, ..., 5, 5, 5])

    >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
    array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])

    >>> np.pad(a, (2,), 'maximum')
    array([5, 5, 1, 2, 3, 4, 5, 5, 5])

    >>> np.pad(a, (2,), 'mean')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> np.pad(a, (2,), 'median')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> a = [[1, 2], [3, 4]]
    >>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
    array([[1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [3, 3, 3, 4, 3, 3, 3],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1]])

    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'reflect')
    array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])

    >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
    array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])

    >>> np.pad(a, (2, 3), 'symmetric')
    array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])

    >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
    array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])

    >>> np.pad(a, (2, 3), 'wrap')
    array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])

    >>> def pad_with(vector, pad_width, iaxis, kwargs):
    ...     pad_value = kwargs.get('padder', 10)
    ...     vector[:pad_width[0]] = pad_value
    ...     vector[-pad_width[1]:] = pad_value
    >>> a = np.arange(6)
    >>> a = a.reshape((2, 3))
    >>> np.pad(a, 2, pad_with)
    array([[10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10,  0,  1,  2, 10, 10],
           [10, 10,  3,  4,  5, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10]])
    >>> np.pad(a, 2, pad_with, padder=100)
    array([[100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100,   0,   1,   2, 100, 100],
           [100, 100,   3,   4,   5, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100]])
    �
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