code: def pretrained_cfg_for_features(pretrained_cfg):
      pretrained_cfg = deepcopy(pretrained_cfg)
      # remove default pretrained cfg fields that don't have much relevance for feature backbone
      to_remove = (
          "num_classes",
          "classifier",
          "global_pool",
      )  # add default final pool size?
      for tr in to_remove:
          pretrained_cfg.pop(tr, None)
      return pretrained_cfg
  
imports: import dataclasses
  import logging
  import os
  from copy import deepcopy
  from typing import Optional, Dict, Callable, Any, Tuple
  from torch import nn
  from torch.hub import load_state_dict_from_url
  from timm.models._features import FeatureListNet, FeatureHookNet
  from timm.models._features_fx import FeatureGraphNet
  from timm.models._helpers import load_state_dict
  from timm.models._hub import has_hf_hub, download_cached_file, check_cached_file, load_state_dict_from_hf
  from timm.models._manipulate import adapt_input_conv
  from timm.models._pretrained import PretrainedCfg
  from timm.models._prune import adapt_model_from_file
  from timm.models._registry import get_pretrained_cfg
label_desc: Use image node_id 6 for calling a global function with example usage: pretrained_cfg_for_features(pretrained_cfg) and returns: pretrained_cfg