def diagonal(
    a: ndarray,
    offset: int = 0,
    axis1: int = 0,
    axis2: int = 1,
    extract: bool = True,
) -> ndarray:
    """
    diagonal(a: ndarray, offset=0, axis1=None, axis2=None)

    Return specified diagonals.

    If `a` is 2-D, returns the diagonal of `a` with the given offset,
    i.e., the collection of elements of the form ``a[i, i+offset]``.  If
    `a` has more than two dimensions, then the axes specified by `axis1`
    and `axis2` are used to determine the 2-D sub-array whose diagonal is
    returned.  The shape of the resulting array can be determined by
    removing `axis1` and `axis2` and appending an index to the right equal
    to the size of the resulting diagonals.

    Parameters
    ----------
    a : array_like
        Array from which the diagonals are taken.
    offset : int, optional
        Offset of the diagonal from the main diagonal.  Can be positive or
        negative.  Defaults to main diagonal (0).
    axis1 : int, optional
        Axis to be used as the first axis of the 2-D sub-arrays from which
        the diagonals should be taken.  Defaults to first axis (0).
    axis2 : int, optional
        Axis to be used as the second axis of the 2-D sub-arrays from
        which the diagonals should be taken. Defaults to second axis (1).

    Returns
    -------
    array_of_diagonals : ndarray
        If `a` is 2-D, then a 1-D array containing the diagonal and of the
        same type as `a` is returned unless `a` is a `matrix`, in which case
        a 1-D array rather than a (2-D) `matrix` is returned in order to
        maintain backward compatibility.

        If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`
        are removed, and a new axis inserted at the end corresponding to the
        diagonal.

    Raises
    ------
    ValueError
        If the dimension of `a` is less than 2.

    Notes
    -----
    Unlike NumPy's, the cuPyNumeric implementation always returns a copy

    See Also
    --------
    numpy.diagonal

    Availability
    --------
    Multiple GPUs, Multiple CPUs

    """
    return a.diagonal(offset=offset, axis1=axis1, axis2=axis2, extract=extract)

    diagonal(a: ndarray, offset=0, axis1=None, axis2=None)

    Return specified diagonals.

    If `a` is 2-D, returns the diagonal of `a` with the given offset,
    i.e., the collection of elements of the form ``a[i, i+offset]``.  If
    `a` has more than two dimensions, then the axes specified by `axis1`
    and `axis2` are used to determine the 2-D sub-array whose diagonal is
    returned.  The shape of the resulting array can be determined by
    removing `axis1` and `axis2` and appending an index to the right equal
    to the size of the resulting diagonals.

    Parameters
    ----------
    a : array_like
        Array from which the diagonals are taken.
    offset : int, optional
        Offset of the diagonal from the main diagonal.  Can be positive or
        negative.  Defaults to main diagonal (0).
    axis1 : int, optional
        Axis to be used as the first axis of the 2-D sub-arrays from which
        the diagonals should be taken.  Defaults to first axis (0).
    axis2 : int, optional
        Axis to be used as the second axis of the 2-D sub-arrays from
        which the diagonals should be taken. Defaults to second axis (1).

    Returns
    -------
    array_of_diagonals : ndarray
        If `a` is 2-D, then a 1-D array containing the diagonal and of the
        same type as `a` is returned unless `a` is a `matrix`, in which case
        a 1-D array rather than a (2-D) `matrix` is returned in order to
        maintain backward compatibility.

        If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`
        are removed, and a new axis inserted at the end corresponding to the
        diagonal.

    Raises
    ------
    ValueError
        If the dimension of `a` is less than 2.

    Notes
    -----
    Unlike NumPy's, the cuPyNumeric implementation always returns a copy

    See Also
    --------
    numpy.diagonal

    Availability
    --------
    Multiple GPUs, Multiple CPUs

    
cupynumeric/_module/indexing.py