code: def get_num_heads(cls, module_name: str, model: Module) -> int:
      if cls.is_attention(
          module_name, include_output=True
      ) or cls.is_attention_layer(module_name):
          for pattern in cls.ATTENTION:
              match = re.search(pattern, module_name)
              if match:
                  attention_module_name = module_name[
                      0 : match.span()[1]
                  ]
                  module = get_nested_attr(model, attention_module_name)
                  if hasattr(module, "self") and hasattr(
                      getattr(module, "self"), "num_attention_heads"
                  ):
                      num_heads = module.self.num_attention_heads
                  elif hasattr(module, "num_heads"):
                      num_heads = module.num_heads
                  elif hasattr(module, "n_heads"):
                      num_heads = module.n_heads
                  else:
                      warn_msg = f"Can not get the heads number of attention layer : `{attention_module_name}`."
                      _logger.warning(warn_msg)
                      num_heads = 0
                  return num_heads
          return 0
      else:
          warn_msg = f"The layer `{module_name}` might not an (Q|K|V) attention layer."
          _logger.warning(warn_msg)
          return 0
  
imports: from __future__ import annotations
  import logging
  import re
  from typing import Tuple
  from torch.nn import Module
  from nni.compression.utils import get_nested_attr
label_desc: Use image node_id 5 for calling the HuggingfaceModelParser obj's underlying member method code with example usage: obj.get_num_heads(cls, module_name, model) and returns: int, int, num_heads