def call(
        self,
        input_features: TFModelInputType | None = None,
        decoder_input_ids: np.ndarray | tf.Tensor | None = None,
        decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        decoder_position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        decoder_head_mask: np.ndarray | tf.Tensor | None = None,
        cross_attn_head_mask: np.ndarray | tf.Tensor | None = None,
        encoder_outputs: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
        decoder_inputs_embeds: Optional[Tuple[Union[np.ndarray, tf.Tensor]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
        r"""
        Returns:

        Example:

         ```python
         >>> import tensorflow as tf
         >>> from transformers import TFWhisperModel, AutoFeatureExtractor
         >>> from datasets import load_dataset

         >>> model = TFWhisperModel.from_pretrained("openai/whisper-base")
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
         >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="tf")
         >>> input_features = inputs.input_features
         >>> decoder_input_ids = tf.convert_to_tensor([[1, 1]]) * model.config.decoder_start_token_id
         >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
         >>> list(last_hidden_state.shape)
         [1, 2, 512]
         ```"""
        outputs = self.model(
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            decoder_position_ids=decoder_position_ids,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            encoder_outputs=encoder_outputs,
            past_key_values=past_key_values,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        return outputs

        Returns:

        Example:

         ```python
         >>> import tensorflow as tf
         >>> from transformers import TFWhisperModel, AutoFeatureExtractor
         >>> from datasets import load_dataset

         >>> model = TFWhisperModel.from_pretrained("openai/whisper-base")
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
         >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="tf")
         >>> input_features = inputs.input_features
         >>> decoder_input_ids = tf.convert_to_tensor([[1, 1]]) * model.config.decoder_start_token_id
         >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
         >>> list(last_hidden_state.shape)
         [1, 2, 512]
         ```
dpkd/transformers/src/transformers/models/whisper/modeling_tf_whisper.py