docstr: Transfer the weights of `self.src` to `self.dest` by performing a forward pass using `x` as input. Under the
  hood we tracked all the operations in both modules.
docstr_tok: ["Transfer","the","weights","of","`","self.src","`","to","`","self.dest","`","by","performing","a","forward","pass","using","`","x","`","as","input",".","Under","the","hood","we","tracked","all","the","operations","in","both","modules","."]
code: def __call__(self, x: Tensor):
      """
      Transfer the weights of `self.src` to `self.dest` by performing a forward pass using `x` as input. Under the
      hood we tracked all the operations in both modules.
      """
      dest_traced = Tracker(self.dest)(x).parametrized
      src_traced = Tracker(self.src)(x).parametrized
  
      src_traced = list(
          filter(lambda x: type(x) not in self.src_skip, src_traced)
      )
      dest_traced = list(
          filter(lambda x: type(x) not in self.dest_skip, dest_traced)
      )
  
      if len(dest_traced) != len(src_traced):
          raise Exception(
              f"Numbers of operations are different. Source module has {len(src_traced)} operations while"
              f" destination module has {len(dest_traced)}."
          )
  
      for dest_m, src_m in zip(dest_traced, src_traced):
          dest_m.load_state_dict(src_m.state_dict())
          if self.verbose == 1:
              print(f"Transfered from={src_m} to={dest_m}")
  
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 the ModuleTransfer obj's underlying member method code with example usage: obj.__call__(x) without return types