code: def check_frozen_norm_layer(
      model: torch.nn.Module,
  ) -> Tuple[bool, int]:
      from cvnets.layers import norm_layers_tuple
  
      unwrapped_model = unwrap_model_fn(model)
  
      count_norm = 0
      frozen_state = False
      for m in unwrapped_model.modules():
          if isinstance(m, norm_layers_tuple):
              frozen_state = m.weight.requires_grad
  
      return frozen_state, count_norm
  
file_path: ml-cvnets/utils/common_utils.py
imports: import os
  import random
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import torch
  from packaging import version
  from torch import Tensor
  from common import MIN_TORCH_VERSION
  from utils import logger
  from utils.ddp_utils import is_master
label_desc: Use image node_id 3 for calling a global function with example usage: check_frozen_norm_layer(model) and returns: frozen_state,  count_norm