def forward(
        self,
        pixel_values: torch.Tensor,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> BackboneOutput:
        """
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, AutoBackbone
        >>> import torch
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
        >>> model = AutoBackbone.from_pretrained(
        ...     "facebook/dinov2-base", out_features=["stage2", "stage5", "stage8", "stage11"]
        ... )

        >>> inputs = processor(image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> feature_maps = outputs.feature_maps
        >>> list(feature_maps[-1].shape)
        [1, 768, 16, 16]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        embedding_output = self.embeddings(pixel_values)

        outputs = self.encoder(
            embedding_output, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict
        )

        hidden_states = outputs.hidden_states if return_dict else outputs[1]

        feature_maps = ()
        for stage, hidden_state in zip(self.stage_names, hidden_states):
            if stage in self.out_features:
                if self.config.apply_layernorm:
                    hidden_state = self.layernorm(hidden_state)
                if self.config.reshape_hidden_states:
                    hidden_state = hidden_state[:, 1:]
                    # this was actually a bug in the original implementation that we copied here,
                    # cause normally the order is height, width
                    batch_size, _, height, width = pixel_values.shape
                    patch_size = self.config.patch_size
                    hidden_state = hidden_state.reshape(batch_size, height // patch_size, width // patch_size, -1)
                    hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
                feature_maps += (hidden_state,)

        if not return_dict:
            if output_hidden_states:
                output = (feature_maps,) + outputs[1:]
            else:
                output = (feature_maps,) + outputs[2:]
            return output

        return BackboneOutput(
            feature_maps=feature_maps,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=outputs.attentions if output_attentions else None,
        )

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, AutoBackbone
        >>> import torch
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
        >>> model = AutoBackbone.from_pretrained(
        ...     "facebook/dinov2-base", out_features=["stage2", "stage5", "stage8", "stage11"]
        ... )

        >>> inputs = processor(image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> feature_maps = outputs.feature_maps
        >>> list(feature_maps[-1].shape)
        [1, 768, 16, 16]
        ```
dpkd/transformers/src/transformers/models/dinov2/modeling_dinov2.py