def __call__(
        self,
        inputs: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        decoder_input_ids: Optional[jnp.ndarray] = None,
        decoder_attention_mask: Optional[jnp.ndarray] = None,
        decoder_position_ids: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        freeze_feature_encoder: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import FlaxSpeechEncoderDecoderModel, AutoTokenizer

        >>> # load a fine-tuned wav2vec2-2-bart model
        >>> model = FlaxSpeechEncoderDecoderModel.from_pretrained("patrickvonplaten/wav2vec2-2-bart-large")
        >>> # load output tokenizer
        >>> tokenizer_output = AutoTokenizer.from_pretrained("facebook/bart-large")

        >>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)

        >>> # use bart's special bos, pad and eos tokens
        >>> model.config.decoder_start_token_id = model.decoder.config.bos_token_id
        >>> model.config.pad_token_id = model.decoder.config.pad_token_id
        >>> model.config.eos_token_id = model.decoder.config.eos_token_id

        >>> outputs = model.generate(inputs)
        # Assert something? More interesting input? dtype correct?
        ```
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # prepare encoder inputs
        if attention_mask is None:
            attention_mask = jnp.ones_like(inputs, dtype="i4")

        # prepare decoder inputs
        if decoder_input_ids is None:
            raise ValueError(
                "`decoder_input_ids` cannot be `None`. For sequence to sequence training, `decoder_position_ids` must"
                " be specified as an input argument."
            )
        if decoder_attention_mask is None:
            decoder_attention_mask = jnp.ones_like(decoder_input_ids)
        if decoder_position_ids is None:
            batch_size, sequence_length = decoder_input_ids.shape
            decoder_position_ids = jnp.broadcast_to(
                jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
            )

        # Handle any PRNG if needed
        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}

        return self.module.apply(
            {"params": params or self.params},
            inputs=jnp.array(inputs, dtype="f4"),
            attention_mask=jnp.array(attention_mask, dtype="i4"),
            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            freeze_feature_encoder=freeze_feature_encoder,
            rngs=rngs,
        )

        Returns:

        Examples:

        ```python
        >>> from transformers import FlaxSpeechEncoderDecoderModel, AutoTokenizer

        >>> # load a fine-tuned wav2vec2-2-bart model
        >>> model = FlaxSpeechEncoderDecoderModel.from_pretrained("patrickvonplaten/wav2vec2-2-bart-large")
        >>> # load output tokenizer
        >>> tokenizer_output = AutoTokenizer.from_pretrained("facebook/bart-large")

        >>> inputs = jnp.ones((2, 5000), dtype=jnp.float32)

        >>> # use bart's special bos, pad and eos tokens
        >>> model.config.decoder_start_token_id = model.decoder.config.bos_token_id
        >>> model.config.pad_token_id = model.decoder.config.pad_token_id
        >>> model.config.eos_token_id = model.decoder.config.eos_token_id

        >>> outputs = model.generate(inputs)
        # Assert something? More interesting input? dtype correct?
        ```
        
dpkd/transformers/src/transformers/models/speech_encoder_decoder/modeling_flax_speech_encoder_decoder.py