channel_wise option missing in transforms.RandScaleIntensityFixedMean #8363

class RandScaleIntensityFixedMean(RandomizableTransform):
    """
    Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`
    is randomly picked. Subtract the mean intensity before scaling with `factor`, then add the same value after scaling
    to ensure that the output has the same mean as the input.
    """

    backend = ScaleIntensityFixedMean.backend

    def __init__(
        self,
        prob: float = 0.1,
        factors: Sequence[float] | float = 0,
        fixed_mean: bool = True,
        preserve_range: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> None:
        """
        Args:
            factors: factor range to randomly scale by ``v = v * (1 + factor)``.
                if single number, factor value is picked from (-factors, factors).
            preserve_range: clips the output array/tensor to the range of the input array/tensor
            fixed_mean: subtract the mean intensity before scaling with `factor`, then add the same value after scaling
                to ensure that the output has the same mean as the input.
            channel_wise: if True, scale on each channel separately. `preserve_range` and `fixed_mean` are also applied
            on each channel separately if `channel_wise` is True. Please ensure that the first dimension represents the
            channel of the image if True.
            dtype: output data type, if None, same as input image. defaults to float32.

        """