def forward(
        self,
        inputs: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        subsampled_output_points: Optional[Dict[str, torch.Tensor]] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, PerceiverClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import PerceiverForMultimodalAutoencoding
        >>> import torch
        >>> import numpy as np

        >>> # create multimodal inputs
        >>> images = torch.randn((1, 16, 3, 224, 224))
        >>> audio = torch.randn((1, 30720, 1))
        >>> inputs = dict(image=images, audio=audio, label=torch.zeros((images.shape[0], 700)))

        >>> model = PerceiverForMultimodalAutoencoding.from_pretrained("deepmind/multimodal-perceiver")

        >>> # in the Perceiver IO paper, videos are auto-encoded in chunks
        >>> # each chunk subsamples different index dimensions of the image and audio modality decoder queries
        >>> nchunks = 128
        >>> image_chunk_size = np.prod((16, 224, 224)) // nchunks
        >>> audio_chunk_size = audio.shape[1] // model.config.samples_per_patch // nchunks
        >>> # process the first chunk
        >>> chunk_idx = 0
        >>> subsampling = {
        ...     "image": torch.arange(image_chunk_size * chunk_idx, image_chunk_size * (chunk_idx + 1)),
        ...     "audio": torch.arange(audio_chunk_size * chunk_idx, audio_chunk_size * (chunk_idx + 1)),
        ...     "label": None,
        ... }

        >>> outputs = model(inputs=inputs, subsampled_output_points=subsampling)
        >>> logits = outputs.logits
        >>> list(logits["audio"].shape)
        [1, 240]

        >>> list(logits["image"].shape)
        [1, 6272, 3]

        >>> list(logits["label"].shape)
        [1, 700]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.perceiver(
            inputs=inputs,
            attention_mask=attention_mask,
            subsampled_output_points=subsampled_output_points,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        logits = outputs.logits if return_dict else outputs[0]

        loss = None
        if labels is not None:
            raise NotImplementedError("Multimodal autoencoding training is not yet supported")

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return PerceiverClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import PerceiverForMultimodalAutoencoding
        >>> import torch
        >>> import numpy as np

        >>> # create multimodal inputs
        >>> images = torch.randn((1, 16, 3, 224, 224))
        >>> audio = torch.randn((1, 30720, 1))
        >>> inputs = dict(image=images, audio=audio, label=torch.zeros((images.shape[0], 700)))

        >>> model = PerceiverForMultimodalAutoencoding.from_pretrained("deepmind/multimodal-perceiver")

        >>> # in the Perceiver IO paper, videos are auto-encoded in chunks
        >>> # each chunk subsamples different index dimensions of the image and audio modality decoder queries
        >>> nchunks = 128
        >>> image_chunk_size = np.prod((16, 224, 224)) // nchunks
        >>> audio_chunk_size = audio.shape[1] // model.config.samples_per_patch // nchunks
        >>> # process the first chunk
        >>> chunk_idx = 0
        >>> subsampling = {
        ...     "image": torch.arange(image_chunk_size * chunk_idx, image_chunk_size * (chunk_idx + 1)),
        ...     "audio": torch.arange(audio_chunk_size * chunk_idx, audio_chunk_size * (chunk_idx + 1)),
        ...     "label": None,
        ... }

        >>> outputs = model(inputs=inputs, subsampled_output_points=subsampling)
        >>> logits = outputs.logits
        >>> list(logits["audio"].shape)
        [1, 240]

        >>> list(logits["image"].shape)
        [1, 6272, 3]

        >>> list(logits["label"].shape)
        [1, 700]
        ```
dpkd/transformers/src/transformers/models/perceiver/modeling_perceiver.py