def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
        resolution images.

        Source:
        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
        """

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1
        if num_patches == num_positions and height == width:
            return self.position_embeddings
        class_pos_embed = self.position_embeddings[:, 0]
        patch_pos_embed = self.position_embeddings[:, 1:]
        dim = embeddings.shape[-1]
        h0 = height // self.config.patch_size
        w0 = width // self.config.patch_size
        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        h0, w0 = h0 + 0.1, w0 + 0.1
        patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
            mode="bicubic",
            align_corners=False,
        )
        assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)

        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
        resolution images.

        Source:
        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
        
dpkd/transformers/src/transformers/models/vit/modeling_vit.py