def nanmean(
    a: ndarray,
    axis: int | tuple[int, ...] | None = None,
    dtype: np.dtype[Any] | None = None,
    out: ndarray | None = None,
    keepdims: bool = False,
    where: ndarray | None = None,
) -> ndarray:
    """

    Compute the arithmetic mean along the specified axis, ignoring NaNs.

    Returns the average of the array elements.  The average is taken over
    the flattened array by default, otherwise over the specified axis.
    `float64` intermediate and return values are used for integer inputs.

    Parameters
    ----------
    a : array_like
        Array containing numbers whose mean is desired. If `a` is not an
        array, a conversion is attempted.
    axis : None or int or tuple[int], optional
        Axis or axes along which the means are computed. The default is to
        compute the mean of the flattened array.

        If this is a tuple of ints, a mean is performed over multiple axes,
        instead of a single axis or all the axes as before.
    dtype : data-type, optional
        Type to use in computing the mean.  For integer inputs, the default
        is `float64`; for floating point inputs, it is the same as the
        input dtype.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    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.


    where : array_like of bool, optional
        Elements to include in the mean.

    Returns
    -------
    m : ndarray
        If `out is None`, returns a new array of the same dtype as a above
        containing the mean values, otherwise a reference to the output
        array is returned.

    See Also
    --------
    numpy.nanmean

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    """
    return a._nanmean(
        axis=axis, dtype=dtype, out=out, keepdims=keepdims, where=where
    )


    Compute the arithmetic mean along the specified axis, ignoring NaNs.

    Returns the average of the array elements.  The average is taken over
    the flattened array by default, otherwise over the specified axis.
    `float64` intermediate and return values are used for integer inputs.

    Parameters
    ----------
    a : array_like
        Array containing numbers whose mean is desired. If `a` is not an
        array, a conversion is attempted.
    axis : None or int or tuple[int], optional
        Axis or axes along which the means are computed. The default is to
        compute the mean of the flattened array.

        If this is a tuple of ints, a mean is performed over multiple axes,
        instead of a single axis or all the axes as before.
    dtype : data-type, optional
        Type to use in computing the mean.  For integer inputs, the default
        is `float64`; for floating point inputs, it is the same as the
        input dtype.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    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.


    where : array_like of bool, optional
        Elements to include in the mean.

    Returns
    -------
    m : ndarray
        If `out is None`, returns a new array of the same dtype as a above
        containing the mean values, otherwise a reference to the output
        array is returned.

    See Also
    --------
    numpy.nanmean

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    
cupynumeric/_module/stats_avgs_vars.py