def METHOD_NAME(x, weight=None, ddof=0, axis=None, *, keepdims=False, mask_identity=True):
    """
    Args:
        x: The data on which to compute the standard deviation (anything #ak.to_layout recognizes).
        weight: Data that can be broadcasted to `x` to give each value a
            weight. Weighting values equally is the same as no weights;
            weighting some values higher increases the significance of those
            values. Weights can be zero or negative.
        ddof (int): "delta degrees of freedom": the divisor used in the
            calculation is `sum(weights) - ddof`. Use this for "reduced
            standard deviation."
        axis (None or int): If None, combine all values from the array into
            a single scalar result; if an int, group by that axis: `0` is the
            outermost, `1` is the first level of nested lists, etc., and
            negative `axis` counts from the innermost: `-1` is the innermost,
            `-2` is the next level up, etc.
        keepdims (bool): If False, this function decreases the number of
            dimensions by 1; if True, the output values are wrapped in a new
            length-1 dimension so that the result of this operation may be
            broadcasted with the original array.
        mask_identity (bool): If True, the application of this function on
            empty lists results in None (an option type); otherwise, the
            calculation is followed through with the reducers' identities,
            usually resulting in floating-point `nan`.
    Like #ak.std, but treating NaN ("not a number") values as missing.
    Equivalent to
        ak.std(ak.nan_to_none(array))
    with all other arguments unchanged.
    See also #ak.std.
    """
    # Dispatch
    yield x, weight
    # Implementation
    if weight is not None:
        weight = ak.operations.ak_nan_to_none._impl(weight, False, None)
    return _impl(
        ak.operations.ak_nan_to_none._impl(x, False, None),
        weight,
        ddof,
        axis,
        keepdims,
        mask_identity,
    )