class_docstr: Implement the standardisation with mean and standard deviation.
class_docstr_tok: ["Implement","the","standardisation","with","mean","and","standard","deviation","."]
docstr: Apply StandardisationMeanStd inputs `x`.
  
  :param x: Input samples to standardise.
  :param y: Label data, will not be affected by this preprocessing.
  :return: Standardise input samples and unmodified labels.
docstr_tok: ["Apply","StandardisationMeanStd","inputs","`","x","`",".",":","param","x",":","Input","samples","to","standardise",".",":","param","y",":","Label","data",",","will","not","be","affected","by","this","preprocessing",".",":","return",":","Standardise","input","samples","and","unmodified","labels","."]
code: def __call__(
      self,
      x: np.ndarray,
      y: Optional[np.ndarray] = None,
  ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
      """
      Apply StandardisationMeanStd inputs `x`.
  
      :param x: Input samples to standardise.
      :param y: Label data, will not be affected by this preprocessing.
      :return: Standardise input samples and unmodified labels.
      """
      if x.dtype in [
          np.uint8,
          np.uint16,
          np.uint32,
          np.uint64,
      ]:  # pragma: no cover
          raise TypeError(
              f"The data type of input data `x` is {x.dtype} and cannot represent negative values. Consider "
              f"changing the data type of the input data `x` to a type that supports negative values e.g. "
              f"np.float32."
          )
  
      if self._broadcastable_mean is None:
          (
              self._broadcastable_mean,
              self._broadcastable_std,
          ) = broadcastable_mean_std(x, self.mean, self.std)
  
      x_norm = x - self._broadcastable_mean
      x_norm = x_norm / self._broadcastable_std
      x_norm = x_norm.astype(ART_NUMPY_DTYPE)
  
      return x_norm, y
  
file_path: adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/numpy.py
imports: import logging
  from typing import Optional, Tuple, Union
  import numpy
  from art.config import ART_NUMPY_DTYPE
  from art.preprocessing.preprocessing import Preprocessor
  from art.preprocessing.standardisation_mean_std.utils import broadcastable_mean_std
label_desc: Use image node_id 2 for calling the StandardisationMeanStd obj's underlying member method code with example usage: obj.__call__(x, y) and returns: x_norm,  y