code: def broadcastable_mean_std(
      x: Union[np.ndarray, "torch.Tensor", "tf.Tensor"],
      mean: np.ndarray,
      std: np.ndarray,
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Ensure that the mean and standard deviation are broadcastable with respect to input `x`.
  
      :param x: Input samples to standardise.
      :param mean: Mean.
      :param std: Standard Deviation.
      """
      if mean.shape != std.shape:  # pragma: no cover
          raise ValueError(
              "The shape of mean and the standard deviation must be identical."
          )
  
      # catch non-broadcastable input, when mean and std are vectors
      if (
          mean.ndim == 1
          and mean.shape[0] > 1
          and mean.shape[0] != x.shape[-1]
      ):
          # allow input shapes NC* (batch) and C* (non-batch)
          channel_idx = 1 if x.shape[1] == mean.shape[0] else 0
          broadcastable_shape = [1] * x.ndim
          broadcastable_shape[channel_idx] = mean.shape[0]
  
          # expand mean and std to new shape
          mean = mean.reshape(broadcastable_shape)
          std = std.reshape(broadcastable_shape)
      return mean, std
  
label_desc: Use image node_id 1 for calling a global function with example usage: broadcastable_mean_std(x, mean, std) and returns: mean,  std