code: def load_pretrained_weights(
      model,
      model_name,
      weights_path=None,
      load_fc=True,
      advprop=False,
      verbose=True,
  ):
      """Loads pretrained weights from weights path or download using url.
  
      Args:
          model (Module): The whole model of efficientnet.
          model_name (str): Model name of efficientnet.
          weights_path (None or str):
              str: path to pretrained weights file on the local disk.
              None: use pretrained weights downloaded from the Internet.
          load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.
          advprop (bool): Whether to load pretrained weights
                          trained with advprop (valid when weights_path is None).
      """
      if isinstance(weights_path, str):
          state_dict = torch.load(weights_path)
      else:
          # AutoAugment or Advprop (different preprocessing)
          url_map_ = url_map_advprop if advprop else url_map
          state_dict = model_zoo.load_url(url_map_[model_name])
  
      if load_fc:
          ret = model.load_state_dict(state_dict, strict=False)
          assert (
              not ret.missing_keys
          ), "Missing keys when loading pretrained weights: {}".format(
              ret.missing_keys
          )
      else:
          state_dict.pop("_fc.weight")
          state_dict.pop("_fc.bias")
          ret = model.load_state_dict(state_dict, strict=False)
          assert set(ret.missing_keys) == set(
              ["_fc.weight", "_fc.bias"]
          ), "Missing keys when loading pretrained weights: {}".format(
              ret.missing_keys
          )
      assert (
          not ret.unexpected_keys
      ), "Missing keys when loading pretrained weights: {}".format(
          ret.unexpected_keys
      )
  
      if verbose:
          print("Loaded pretrained weights for {}".format(model_name))
  
imports: import re
  import math
  import collections
  from functools import partial
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.utils import model_zoo
label_desc: Use image node_id 11 for calling a global function with example usage: load_pretrained_weights(model, model_name, weights_path, load_fc, advprop, verbose) without return types