code: def download_mobilenet_v3_torchvision_weights(output_path: str):
      """
      Download the Torchvision weights for MobileNet-V3 and save
      them in VISSL compatible format
      """
  
      # Download the weights from torchvision
      tv_model = tv_mobilenet_v3_large(pretrained=True)
  
      # Separate the weights in head and trunk for VISSL
      trunk = {}
      heads = {}
      for k, v in tv_model.state_dict().items():
          if k.startswith("classifier"):
              heads[f"0.{k}"] = v
          else:
              k = f"model.{k}"
              trunk[k] = v
  
      # Save the checkpoint in VISSL format
      out_cp = {
          "classy_state_dict": {
              "base_model": {
                  "model": {
                      "trunk": trunk,
                      "heads": heads,
                  }
              }
          }
      }
      torch.save(out_cp, output_path)
  
imports: from functools import partial
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.models.mobilenetv3 import mobilenetv3_large_100
  from torchvision.models import mobilenet_v3_large
  from vissl.config import AttrDict
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 1 for calling a global function with example usage: download_mobilenet_v3_torchvision_weights(output_path) without return types