code: def get_logger_dict(
      mod: nn.Module, prefix: str = ""
  ) -> Dict[str, Dict]:
      r"""Traverse the modules and save all logger stats into target dict.
      This is mainly used for quantization accuracy debug.
  
      Type of loggers supported:
          ShadowLogger: used to log the outputs of the quantized module and its matching float shadow module,
          OutputLogger: used to log the outputs of the modules
  
      Args:
          mod: module we want to save all logger stats
          prefix: prefix for the current module
  
      Return:
          target_dict: the dictionary used to save all logger stats
  
      """
      torch._C._log_api_usage_once(
          "quantization_api._numeric_suite.get_logger_dict"
      )
  
      target_dict: Dict[str, Dict] = {}
      _get_logger_dict_helper(mod, target_dict, prefix)
      return target_dict
  
imports: import torch
  import torch.nn
  import torch.ao.nn.quantized
  import torch.ao.nn.quantized.dynamic
  from torch.ao.quantization import prepare
  from typing import Dict, List, Optional, Any, Union, Callable, Set
  from torch.ao.quantization.quantization_mappings import get_default_compare_output_module_list
label_desc: Use image node_id 4 for calling a global function with example usage: get_logger_dict(mod, prefix) and returns: target_dict