def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, VisionEncoderDecoderModel
        >>> import requests
        >>> from PIL import Image
        >>> import torch

        >>> processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten")
        >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

        >>> # load image from the IAM dataset
        >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

        >>> # training
        >>> model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
        >>> model.config.pad_token_id = processor.tokenizer.pad_token_id
        >>> model.config.vocab_size = model.config.decoder.vocab_size

        >>> pixel_values = processor(image, return_tensors="pt").pixel_values
        >>> text = "hello world"
        >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids
        >>> outputs = model(pixel_values=pixel_values, labels=labels)
        >>> loss = outputs.loss

        >>> # inference (generation)
        >>> generated_ids = model.generate(pixel_values)
        >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        if encoder_outputs is None:
            if pixel_values is None:
                raise ValueError("You have to specify pixel_values")

            encoder_outputs = self.encoder(
                pixel_values,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                **kwargs_encoder,
            )
        elif isinstance(encoder_outputs, tuple):
            encoder_outputs = BaseModelOutput(*encoder_outputs)

        encoder_hidden_states = encoder_outputs[0]

        # optionally project encoder_hidden_states
        if (
            self.encoder.config.hidden_size != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)

        # else:
        encoder_attention_mask = None

        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            use_cache=use_cache,
            past_key_values=past_key_values,
            return_dict=return_dict,
            **kwargs_decoder,
        )

        # Compute loss independent from decoder (as some shift the logits inside them)
        loss = None
        if labels is not None:
            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1))

        if not return_dict:
            if loss is not None:
                return (loss,) + decoder_outputs + encoder_outputs
            else:
                return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=loss,
            logits=decoder_outputs.logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, VisionEncoderDecoderModel
        >>> import requests
        >>> from PIL import Image
        >>> import torch

        >>> processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten")
        >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

        >>> # load image from the IAM dataset
        >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

        >>> # training
        >>> model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
        >>> model.config.pad_token_id = processor.tokenizer.pad_token_id
        >>> model.config.vocab_size = model.config.decoder.vocab_size

        >>> pixel_values = processor(image, return_tensors="pt").pixel_values
        >>> text = "hello world"
        >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids
        >>> outputs = model(pixel_values=pixel_values, labels=labels)
        >>> loss = outputs.loss

        >>> # inference (generation)
        >>> generated_ids = model.generate(pixel_values)
        >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        ```
dpkd/transformers/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py