code: def __init__(self):
      super().__init__()
      self.global_config: Optional[QuantizationConfig] = None
      self.operator_type_config: Dict[
          torch._ops.OpOverloadPacket, Optional[QuantizationConfig]
      ] = {}
      self.module_type_config: Dict[
          Callable, Optional[QuantizationConfig]
      ] = {}
      self.module_name_config: Dict[
          str, Optional[QuantizationConfig]
      ] = {}
  
imports: from __future__ import annotations
  import copy
  import functools
  from typing import Any, Callable, Dict, List, Optional, Set
  import torch
  import torch._dynamo
  import torch.nn.functional
  from torch.ao.quantization.fake_quantize import FakeQuantize, FusedMovingAvgObsFakeQuantize
  from torch.ao.quantization.observer import HistogramObserver, MinMaxObserver, MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver, PerChannelMinMaxObserver, PlaceholderObserver
  from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
  from torch.ao.quantization.quantizer import QuantizationSpec, Quantizer
  from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import _convert_scalars_to_attrs, OP_TO_ANNOTATOR, OperatorConfig, OperatorPatternType, propagate_annotation, QuantizationConfig
  from torch.fx import Node
label_desc: Use image node_id 1 to create a new XNNPACKQuantizer object from inherited base classes: Quantizer with example: obj = XNNPACKQuantizer()