code: def interpolate_embeddings(
      image_size: int,
      patch_size: int,
      model_state: "OrderedDict[str, torch.Tensor]",
      interpolation_mode: str = "bicubic",
      reset_heads: bool = False,
  ) -> "OrderedDict[str, torch.Tensor]":
      """This function helps interpolate positional embeddings during checkpoint loading,
      especially when you want to apply a pre-trained model on images with different resolution.
  
      Args:
          image_size (int): Image size of the new model.
          patch_size (int): Patch size of the new model.
          model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model.
          interpolation_mode (str): The algorithm used for upsampling. Default: bicubic.
          reset_heads (bool): If true, not copying the state of heads. Default: False.
  
      Returns:
          OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model.
      """
      # Shape of pos_embedding is (1, seq_length, hidden_dim)
      pos_embedding = model_state["encoder.pos_embedding"]
      n, seq_length, hidden_dim = pos_embedding.shape
      if n != 1:
          raise ValueError(
              f"Unexpected position embedding shape: {pos_embedding.shape}"
          )
  
      new_seq_length = (image_size // patch_size) ** 2 + 1
  
      # Need to interpolate the weights for the position embedding.
      # We do this by reshaping the positions embeddings to a 2d grid, performing
      # an interpolation in the (h, w) space and then reshaping back to a 1d grid.
      if new_seq_length != seq_length:
          # The class token embedding shouldn't be interpolated, so we split it up.
          seq_length -= 1
          new_seq_length -= 1
          pos_embedding_token = pos_embedding[:, :1, :]
          pos_embedding_img = pos_embedding[:, 1:, :]
  
          # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length)
          pos_embedding_img = pos_embedding_img.permute(0, 2, 1)
          seq_length_1d = int(math.sqrt(seq_length))
          if seq_length_1d * seq_length_1d != seq_length:
              raise ValueError(
                  f"seq_length is not a perfect square! Instead got seq_length_1d * seq_length_1d = {seq_length_1d * seq_length_1d } and seq_length = {seq_length}"
              )
  
          # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d)
          pos_embedding_img = pos_embedding_img.reshape(
              1, hidden_dim, seq_length_1d, seq_length_1d
          )
          new_seq_length_1d = image_size // patch_size
  
          # Perform interpolation.
          # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d)
          new_pos_embedding_img = nn.functional.interpolate(
              pos_embedding_img,
              size=new_seq_length_1d,
              mode=interpolation_mode,
              align_corners=True,
          )
  
          # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length)
          new_pos_embedding_img = new_pos_embedding_img.reshape(
              1, hidden_dim, new_seq_length
          )
  
          # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim)
          new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1)
          new_pos_embedding = torch.cat(
              [pos_embedding_token, new_pos_embedding_img], dim=1
          )
  
          model_state["encoder.pos_embedding"] = new_pos_embedding
  
          if reset_heads:
              model_state_copy: "OrderedDict[str, torch.Tensor]" = (
                  OrderedDict()
              )
              for k, v in model_state.items():
                  if not k.startswith("heads"):
                      model_state_copy[k] = v
              model_state = model_state_copy
  
      return model_state
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Any, Callable, Dict, List, NamedTuple, Optional
  import torch
  import torch.nn
  from ..ops.misc import Conv2dNormActivation, MLP
  from ..transforms._presets import ImageClassification, InterpolationMode
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 7 for calling a global function with example usage: interpolate_embeddings(image_size, patch_size, model_state, interpolation_mode, reset_heads) and returns: model_state