def __init__(
        self,
        encoding_length: int,
        model: "tf.Tensor",
        channels_first: bool = False,
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        preprocessing_defences: "Preprocessor" | list["Preprocessor"] | None = None,
        postprocessing_defences: "Postprocessor" | list["Postprocessor"] | None = None,
        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
    ):
        """
        Initialization specific to TensorFlow generator implementations.

        :param encoding_length: length of the input seed
        :param 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

        Initialization specific to TensorFlow generator implementations.

        :param encoding_length: length of the input seed
        :param 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.
        
art/estimators/generation/tensorflow.py