def predict(self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs) -> np.ndarray:
        """
        Perform prediction for a batch of inputs.

        :param x: Input samples.
        :param batch_size: Size of batches.
        :param training_mode: `True` for model set to training mode and `False` for model set to evaluation mode.
        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
        """
        # Apply preprocessing
        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)

        # Run prediction with batch processing
        results_list = []
        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
        for m in range(num_batch):
            # Batch indexes
            begin, end = (
                m * batch_size,
                min((m + 1) * batch_size, x_preprocessed.shape[0]),
            )

            # Run prediction
            results_list.append(self._model(x_preprocessed[begin:end], training=training_mode))

        results = np.vstack(results_list)

        # Apply postprocessing
        predictions = self._apply_postprocessing(preds=results, fit=False)
        return predictions

        Perform prediction for a batch of inputs.

        :param x: Input samples.
        :param batch_size: Size of batches.
        :param training_mode: `True` for model set to training mode and `False` for model set to evaluation mode.
        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
        
art/estimators/classification/tensorflow.py