def __init__(
        self,
        input_ph: "tf.Placeholder",
        model: "tf.Tensor",
        loss: "tf.Tensor" | None = None,
        sess: "tf.compat.v1.Session" | None = None,
        channels_first=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),
        feed_dict: dict[Any, Any] | None = None,
    ):
        """
        Initialization specific to TensorFlow generator implementations.

        :param input_ph: The input placeholder.
        :param model: TensorFlow model, neural network or other.
        :param loss: The loss function for which to compute gradients. This parameter is necessary when training the
                     model and when computing gradients w.r.t. the loss function.
        :param sess: Computation session.
        :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.
        :param feed_dict: A feed dictionary for the session run evaluating the classifier. This dictionary includes all
                          additionally required placeholders except the placeholders defined in this class.
        """
        import tensorflow.compat.v1 as tf

        super().__init__(
            model=model,
            clip_values=clip_values,
            channels_first=channels_first,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
        )

        self._input_ph = input_ph
        self._encoding_length = self.input_ph.shape[1]
        self._loss = loss
        if self.loss is not None:
            self._grad = tf.gradients(self.loss, self.input_ph)
        if feed_dict is None:
            self._feed_dict = {}
        else:
            self._feed_dict = feed_dict

        # Assign session
        if sess is None:  # pragma: no cover
            raise ValueError("A session cannot be None.")
            # TODO do the same thing for all not None variables
        self._sess = sess

        Initialization specific to TensorFlow generator implementations.

        :param input_ph: The input placeholder.
        :param model: TensorFlow model, neural network or other.
        :param loss: The loss function for which to compute gradients. This parameter is necessary when training the
                     model and when computing gradients w.r.t. the loss function.
        :param sess: Computation session.
        :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.
        :param feed_dict: A feed dictionary for the session run evaluating the classifier. This dictionary includes all
                          additionally required placeholders except the placeholders defined in this class.
        
art/estimators/generation/tensorflow.py