code: def get_equalization_qconfig_dict(
      layer_sqnr_dict: Dict[str, float], num_layers_to_equalize: int
  ) -> Any:
      """Given the layer to SQNR dictionary, find the layers with the highest
      quantization errors, and return an equalization_qconfig_dict
      specifying to only equalize those top layers.
  
      Args:
          layer_sqnr_dict: Dictionary mapping layer names to SQNR values (found
              when comparing an equalized model against a float model)
          num_layers_to_equalize: Number of layers with the highest quantization
             errors to equalize
      """
  
      # Sort the layer_sqnr_dictionary values and get the layers with the lowest
      # SQNR values (aka highest quantization errors)
      layer_sqnr_sorted = sorted(
          layer_sqnr_dict.items(), key=lambda item: item[1]
      )
      layers_to_equalize = layer_sqnr_sorted[:num_layers_to_equalize]
  
      # Constructs an equalization_qconfig_dict that specifies to only equalize
      # the layers with the highest quantization errors
      module_to_qconfig_list = [
          (item[0], default_equalization_qconfig)
          for item in layers_to_equalize
      ]
      equalization_qconfig_dict = {
          "module_name": module_to_qconfig_list
      }
      return equalization_qconfig_dict
  
imports: import warnings
  from collections import namedtuple
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.ao.nn.intrinsic
  from torch.fx import GraphModule
  from torch.fx.graph import Node
  from torch.ao.quantization.fx.graph_module import _get_observed_graph_module_attr
  from ..observer import _with_args, ObserverBase, PerChannelMinMaxObserver
  from ..utils import _parent_name, check_min_max_valid
  from .utils import get_new_attr_name_with_prefix, maybe_get_next_module, node_arg_is_weight
label_desc: Use image node_id 21 for calling a global function with example usage: get_equalization_qconfig_dict(layer_sqnr_dict, num_layers_to_equalize) and returns: equalization_qconfig_dict