the memory layout of
        the underlying array, and only refer to the order of indexing.
        'A' means to read / write the elements in Fortran-like index
        order if `a` is Fortran *contiguous* in memory, C-like order
        otherwise.

    Returns
    -------
    reshaped_array : ndarray
        This will be a new view object if possible; otherwise, it will
        be a copy.  Note there is no guarantee of the *memory layout* (C- or
        Fortran- contiguous) of the returned array.

    See Also
    --------
    ndarray.reshape : Equivalent method.

    Notes
    -----
    It is not always possible to change the shape of an array without copying
    the data.
    
    The `order` keyword gives the index ordering both for *fetching* the values
    from `a`, and then *placing* the values into the output array.
    For example, let's say you have an array:

    >>> a = np.arange(6).reshape((3, 2))
    >>> a
    array([[0, 1],
           [2, 3],
           [4, 5]])

    You can think of reshaping as first raveling the array (using the given
    index order), then inserting the elements from the raveled array into the
    new array using the same kind of index ordering as was used for the
    raveling.

    >>> np.reshape(a, (2, 3)) # C-like index ordering
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
    array([[0, 4, 3],
           [2, 1, 5]])
    >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
    array([[0, 4, 3],
           [2, 1, 5]])

    Examples
    --------
    >>> a = np.array([[1,2,3], [4,5,6]])
    >>> np.reshape(a, 6)
    array([1, 2, 3, 4, 5, 6])
    >>> np.reshape(a, 6, order='F')
    array([1, 4, 2, 5, 3, 6])

    >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
    array([[1, 2],
           [3, 4],
           [5, 6]])
    r+