code: def copy_parameters(
      from_model: nn.Module, our_model: nn.Module
  ) -> nn.Module:
      # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
      from_state_dict = from_model.state_dict()
      our_state_dict = our_model.state_dict()
      config = our_model.config
      all_keys = []
      for stage_idx in range(len(config.hidden_sizes)):
          for block_id in range(config.depths[stage_idx]):
              from_key = (
                  f"block{stage_idx + 1}.{block_id}.layer_scale_1"
              )
              to_key = f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"
  
              all_keys.append((from_key, to_key))
              from_key = (
                  f"block{stage_idx + 1}.{block_id}.layer_scale_2"
              )
              to_key = f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"
  
              all_keys.append((from_key, to_key))
  
      for from_key, to_key in all_keys:
          our_state_dict[to_key] = from_state_dict.pop(from_key)
  
      our_model.load_state_dict(our_state_dict)
      return our_model
  
imports: import argparse
  import json
  import sys
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from typing import List
  import torch
  import torch.nn
  from torch import Tensor
  from huggingface_hub import cached_download, hf_hub_download
  from transformers import AutoFeatureExtractor, VanConfig, VanForImageClassification
  from transformers.models.van.modeling_van import VanLayerScaling
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: copy_parameters(from_model, our_model) and returns: our_model