class_docstr: This class implements a DGM with the TensorFlow framework.
class_docstr_tok: ["This","class","implements","a","DGM","with","the","TensorFlow","framework","."]
docstr: Initialization specific to TensorFlow generator implementations.
  
  :encoding_length: length of the input seed
  :model: TensorFlow model, neural network or other.
  :param channels_first: Set channels first or last.
  :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
                      maximum values allowed for features. If floats are provided, these will be used as the range
                      of all features. If arrays are provided, each value will be considered the bound for a
                      feature, thus the shape of clip values needs to match the total number of features.
  :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
  :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
  :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
                        used for data preprocessing. The first value will be subtracted from the input. The input
                        will then be divided by the second one.
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code: def __init__(
      self,
      encoding_length: int,
      model: "tf.Tensor",
      channels_first: bool = False,
      clip_values: Optional["CLIP_VALUES_TYPE"] = None,
      preprocessing_defences: Union[
          "Preprocessor", List["Preprocessor"], None
      ] = None,
      postprocessing_defences: Union[
          "Postprocessor", List["Postprocessor"], None
      ] = None,
      preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
  ):
      """
      Initialization specific to TensorFlow generator implementations.
  
      :encoding_length: length of the input seed
      :model: TensorFlow model, neural network or other.
      :param channels_first: Set channels first or last.
      :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
                          maximum values allowed for features. If floats are provided, these will be used as the range
                          of all features. If arrays are provided, each value will be considered the bound for a
                          feature, thus the shape of clip values needs to match the total number of features.
      :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
      :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
      :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
                            used for data preprocessing. The first value will be subtracted from the input. The input
                            will then be divided by the second one.
      """
      super().__init__(
          model=model,
          clip_values=clip_values,
          channels_first=channels_first,
          preprocessing_defences=preprocessing_defences,
          postprocessing_defences=postprocessing_defences,
          preprocessing=preprocessing,
      )
      self._encoding_length = encoding_length
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Any, Dict, List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.generation.generator import GeneratorMixin
  from art.estimators.tensorflow import TensorFlowEstimator, TensorFlowV2Estimator
label_desc: Use image node_id 1 to create a new TensorFlowV2Generator object from inherited base classes: GeneratorMixin, TensorFlowV2Estimator with example: obj = TensorFlowV2Generator(encoding_length, model, channels_first, clip_values, preprocessing_defences, postprocessing_defences, preprocessing)