code: def extract_logger_info(
      model_a: nn.Module,
      model_b: nn.Module,
      logger_cls: Callable,
      model_name_to_use_for_layer_names: str,
  ) -> NSResultsType:
      """
      Traverse all loggers in `model_a` and `model_b`, and extract the logged
      information.
  
      Args:
          model_a: model A
          model_b: model B
          logger_cls: class of Logger to use
          model_name_to_use_for_layer_names: string name of model to use for
            layer names in the output
  
      Return:
          NSResultsType, containing the logged comparisons
      """
      torch._C._log_api_usage_once(
          "quantization_api._numeric_suite_fx.extract_logger_info"
      )
      results: NSResultsType = {}
      for model in (model_a, model_b):
          _extract_logger_info_one_model(model, results, logger_cls)
      # fill in missing fqn entries
      maybe_add_missing_fqns(results)
      # rekey on the name of model b
      results = rekey_logger_info_on_node_name_of_model(
          results, model_name_to_use_for_layer_names
      )
      return results
  
imports: import collections
  import torch
  import torch.nn
  import torch.ao.quantization.quantize_fx
  from torch.fx import GraphModule
  from torch.fx.graph import Node
  from torch.ao.ns.fx.mappings import get_base_name_to_sets_of_related_ops
  from torch.ao.ns.fx.graph_matcher import get_matching_subgraph_pairs, get_type_a_related_to_b
  from .fx.weight_utils import extract_weight_from_node
  from .fx.graph_passes import add_loggers_to_model, create_a_shadows_b
  from .fx.utils import rekey_logger_info_on_node_name_of_model, maybe_add_missing_fqns, get_target_type_str
  from .fx.ns_types import NSSingleResultValuesType, NSResultsType, NSNodeTargetType
  from torch.ao.quantization.backend_config.utils import get_fusion_pattern_to_root_node_getter
  from torch.ao.quantization.backend_config import BackendConfig
  from torch.ao.quantization.fx.match_utils import _find_matches
  from torch.ao.quantization.fx.graph_module import _get_observed_graph_module_attr
  from torch.ao.quantization.fx.qconfig_mapping_utils import _generate_node_name_to_qconfig
  from torch.ao.quantization.fx.quantize_handler import _get_pattern_to_quantize_handlers
  from torch.ao.quantization.qconfig import QConfigAny
  from torch.ao.quantization import QConfigMapping
  from torch.ao.ns.fx.n_shadows_utils import OutputProp, _get_dedup_subgraphs, SHADOW_WRAPPER_NODE_NAME_PREFIX, group_results_by_subgraph, create_results_comparison, print_n_shadows_summary, create_n_transformed_and_logged_copies_of_subgraph, create_add_loggers_graph, extract_weight_comparison
  from torch.ao.ns.fx.qconfig_multi_mapping import QConfigMultiMapping
  from typing import Dict, Tuple, Callable, List, Optional, Set, Any, Type
label_desc: Use image node_id 8 for calling a global function with example usage: extract_logger_info(model_a, model_b, logger_cls, model_name_to_use_for_layer_names) and returns: results