def forward(self, hidden_states: List[torch.Tensor], patch_height=None, patch_width=None) -> List[torch.Tensor]:
        """
        Args:
            hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, hidden_size, height, width)`):
                List of hidden states from the backbone.
        """
        if not isinstance(hidden_states, (tuple, list)):
            raise ValueError("hidden_states should be a tuple or list of tensors")

        if len(hidden_states) != len(self.config.neck_hidden_sizes):
            raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.")

        # postprocess hidden states
        if self.reassemble_stage is not None:
            hidden_states = self.reassemble_stage(hidden_states, patch_height, patch_width)

        features = [self.convs[i](feature) for i, feature in enumerate(hidden_states)]

        # fusion blocks
        output = self.fusion_stage(features)

        return output

        Args:
            hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, hidden_size, height, width)`):
                List of hidden states from the backbone.
        
dpkd/transformers/src/transformers/models/dpt/modeling_dpt.py