ts, optional
        Axis or axes along which to operate.  By default, flattened input is
        used.

        .. versionadded:: 1.7.0

        If this is a tuple of ints, the maximum is selected over multiple axes,
        instead of a single axis or all the axes as before.
    out : ndarray, optional
        Alternative output array in which to place the result.  Must
        be of the same shape and buffer length as the expected output.
        See :ref:`ufuncs-output-type` for more details.

    keepdims : bool, optional
        If this is set to True, the axes which are reduced are left
        in the result as dimensions with size one. With this option,
        the result will broadcast correctly against the input array.

        If the default value is passed, then `keepdims` will not be
        passed through to the ``max`` method of sub-classes of
        `ndarray`, however any non-default value will be.  If the
        sub-class' method does not implement `keepdims` any
        exceptions will be raised.

    initial : scalar, optional
        The minimum value of an output element. Must be present to allow
        computation on empty slice. See `~numpy.ufunc.reduce` for details.

        .. versionadded:: 1.15.0

    where : array_like of bool, optional
        Elements to compare for the maximum. See `~numpy.ufunc.reduce`
        for details.

        .. versionadded:: 1.17.0

    Returns
    -------
    max : ndarray or scalar
        Maximum of `a`. If `axis` is None, the result is a scalar value.
        If `axis` is an int, the result is an array of dimension
        ``a.ndim - 1``. If `axis` is a tuple, the result is an array of 
        dimension ``a.ndim - len(axis)``.

    See Also
    --------
    amin :
        The minimum value of an array along a given axis, propagating any NaNs.
    nanmax :
        The maximum value of an array along a given axis, ignoring any NaNs.
    maximum :
        Element-wise maximum of two arrays, propagating any NaNs.
    fmax :
        Element-wise maximum of two arrays, ignoring any NaNs.
    argmax :
        Return the indices of the maximum values.

    nanmin, minimum, fmin

    Notes
    -----
    NaN values are propagated, that is if at least one item is NaN, the
    corresponding max value will be NaN as well. To ignore NaN values
    (MATLAB behavior), please use nanmax.

    Don't use `~numpy.max` for element-wise comparison of 2 arrays; when
    ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
    ``max(a, axis=0)``.

    Examples
    --------
    >>> a = np.arange(4).reshape((2,2))
    >>> a
    array([[0, 1],
           [2, 3]])
    >>> np.max(a)           # Maximum of the flattened array
    3
    >>> np.max(a, axis=0)   # Maxima along the first axis
    array([2, 3])
    >>> np.max(a, axis=1)   # Maxima along the second axis
    array([1, 3])
    >>> np.max(a, where=[False, True], initial=-1, axis=0)
    array([-1,  3])
    >>> b = np.arange(5, dtype=float)
    >>> b[2] = np.NaN
    >>> np.max(b)
    nan
    >>> np.max(b, where=~np.isnan(b), initial=-1)
    4.0
    >>> np.nanmax(b)
    4.0

    You can use an initial value to compute the maximum of an empty slice, or
    to initialize it to a different value:

    >>> np.max([[-50], [10]], axis=-1, initial=0)
    array([ 0, 10])

    Notice that the initial value is used as one of the elements for which the
    maximum is determined, unlike for the default argument Python's max
    function, which is only used for empty iterables.

    >>> np.max([5], initial=6)
    6
    >>> max([5], default=6)
    5
    r!