class_docstr: Mapping from model ops to :class:`torch.ao.quantization.QConfig` s.
  
  The user can specify QConfigs using the following methods (in increasing match priority):
  
      ``set_global`` : sets the global (default) QConfig
  
      ``set_object_type`` : sets the QConfig for a given module type, function, or method name
  
      ``set_module_name_regex`` : sets the QConfig for modules matching the given regex string
  
      ``set_module_name`` : sets the QConfig for modules matching the given module name
  
      ``set_module_name_object_type_order`` : sets the QConfig for modules matching a combination
      of the given module name, object type, and the index at which the module appears
  
  Example usage::
  
      qconfig_mapping = QConfigMapping()
          .set_global(global_qconfig)
          .set_object_type(torch.nn.Linear, qconfig1)
          .set_object_type(torch.nn.ReLU, qconfig1)
          .set_module_name_regex("foo.*bar.*conv[0-9]+", qconfig1)
          .set_module_name_regex("foo.*", qconfig2)
          .set_module_name("module1", qconfig1)
          .set_module_name("module2", qconfig2)
          .set_module_name_object_type_order("foo.bar", torch.nn.functional.linear, 0, qconfig3)
code: def __init__(self):
      # In increasing match priority:
      self.global_qconfig: QConfigAny = None
      self.object_type_qconfigs: OrderedDict[
          Union[Callable, str], QConfigAny
      ] = OrderedDict()
      self.module_name_regex_qconfigs: OrderedDict[
          str, QConfigAny
      ] = OrderedDict()
      self.module_name_qconfigs: OrderedDict[
          str, QConfigAny
      ] = OrderedDict()
      self.module_name_object_type_order_qconfigs: OrderedDict[
          Tuple[str, Callable, int], QConfigAny
      ] = OrderedDict()
  
imports: from __future__ import annotations
  from collections import OrderedDict
  from typing import Any, Callable, Dict, Tuple, Union, List
  import torch
  from .fake_quantize import default_weight_fake_quant, FixedQParamsFakeQuantize
  from .observer import _PartialWrapper, default_fixed_qparams_range_0to1_observer, default_fixed_qparams_range_neg1to1_observer, default_placeholder_observer, default_weight_observer
  from .qconfig import default_reuse_input_qconfig, default_symmetric_qnnpack_qconfig, default_symmetric_qnnpack_qat_qconfig, get_default_qconfig, get_default_qat_qconfig, QConfig, QConfigAny, default_quint8_weight_qconfig
label_desc: Use image node_id 1 to create a new QConfigMapping object  with example: obj = QConfigMapping()