code: def get_custom_module_class_keys(
      custom_module_mapping: Dict[QuantType, Dict[Type, Type]]
  ) -> List[Any]:
      r"""Get all the unique custom module keys in the custom config dict
      e.g.
      Input:
      {
          QuantType.STATIC: {
              CustomModule1: ObservedCustomModule
          },
          QuantType.DYNAMIC: {
              CustomModule2: DynamicObservedCustomModule
          },
          QuantType.WEIGHT_ONLY: {
              CustomModule3: WeightOnlyObservedCustomModule
          },
      }
  
      Output:
      # extract the keys across all inner STATIC, DYNAMIC, and WEIGHT_ONLY dicts
      [CustomModule1, CustomModule2, CustomModule3]
      """
      # using set to dedup
      float_custom_module_classes: Set[Any] = set()
      for quant_mode in [
          QuantType.STATIC,
          QuantType.DYNAMIC,
          QuantType.WEIGHT_ONLY,
      ]:
          quant_mode_custom_module_config = custom_module_mapping.get(
              quant_mode, {}
          )
          quant_mode_custom_module_classes = set(
              quant_mode_custom_module_config.keys()
          )
          float_custom_module_classes |= (
              quant_mode_custom_module_classes
          )
      return list(float_custom_module_classes)
  
imports: import copy
  import torch
  import torch.nn
  from torch.ao.quantization import QConfigAny, QuantType
  from torch.ao.quantization.backend_config import DTypeWithConstraints
  from torch.ao.quantization.fake_quantize import FakeQuantizeBase, FixedQParamsFakeQuantize
  from torch.ao.quantization.observer import FixedQParamsObserver, ObserverBase
  from torch.ao.quantization.qconfig import float16_static_qconfig, float16_dynamic_qconfig, qconfig_equals
  from torch.ao.quantization.stubs import DeQuantStub
  from torch.ao.quantization.utils import activation_is_statically_quantized
  from torch.ao.quantization.observer import _is_activation_post_process
  from torch.ao.quantization.qconfig_mapping import QConfigMapping
  from torch.fx import GraphModule, map_arg
  from torch.fx.graph import Graph, Node
  from .custom_config import PrepareCustomConfig
  from ._decomposed import quantized_decomposed_lib
  from typing import Callable, Optional, List, Dict, Any, Set, Tuple, Union, Type
  from dataclasses import dataclass
  from collections import namedtuple
  import operator
  import warnings
label_desc: Use image node_id 3 for calling a global function with example usage: get_custom_module_class_keys(custom_module_mapping) and returns: list