class_docstr: Implement the standardisation with mean and standard deviation.
docstr: Create an instance of StandardisationMeanStdTensorFlow.
  
  :param mean: Mean.
  :param std: Standard Deviation.
code: def __init__(
      self,
      mean: Union[float, np.ndarray] = 0.0,
      std: Union[float, np.ndarray] = 1.0,
      apply_fit: bool = True,
      apply_predict: bool = True,
  ):
      """
      Create an instance of StandardisationMeanStdTensorFlow.
  
      :param mean: Mean.
      :param std: Standard Deviation.
      """
      super().__init__(
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
      self.mean = np.asarray(mean, dtype=ART_NUMPY_DTYPE)
      self.std = np.asarray(std, dtype=ART_NUMPY_DTYPE)
      self._check_params()
  
      # init broadcastable mean and std for lazy loading
      self._broadcastable_mean: Optional[np.ndarray] = None
      self._broadcastable_std: Optional[np.ndarray] = None
  
file_path: adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/tensorflow.py
imports: import logging
  from typing import TYPE_CHECKING, Optional, Tuple, Union
  import numpy
  from art.config import ART_NUMPY_DTYPE
  from art.preprocessing.preprocessing import PreprocessorTensorFlowV2
  from art.preprocessing.standardisation_mean_std.utils import broadcastable_mean_std
label_desc: Use image node_id 1 to create a new StandardisationMeanStdTensorFlow object from inherited base classes: PreprocessorTensorFlowV2 with example: obj = StandardisationMeanStdTensorFlow(mean, std, apply_fit, apply_predict)