class_docstr: Transformers compatible version of torch.quantization.quantize_fx.QuantizationTracer.
  This tracer is used internally to prepare the model for quantization.
code: def __init__(
      self,
      skipped_module_names: List[str],
      skipped_module_classes: List[Callable],
  ):
      super().__init__()
      self.skipped_module_names = skipped_module_names
      self.skipped_module_classes = skipped_module_classes
      # NB: initialized the module_type of top level module to None
      # we are assuming people won't configure the model with the type of top level
      # module here, since people can use "" for global config
      # We can change this if there is a use case that configures
      # qconfig using top level module type
      self.scope = Scope("", None)
      self.node_name_to_scope: Dict[str, Tuple[str, type]] = {}
  
imports: from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import torch
  from torch.fx.node import Argument, Node, Target
  from torch.nn.intrinsic import _FusedModule
  from torch.quantization.fx.graph_module import GraphModule, ObservedGraphModule
  from torch.quantization.quantize_fx import Scope, ScopeContextManager
  from torch.quantization.quantize_fx import fuse_fx
  from torch.quantization.quantize_fx import prepare_fx
  from torch.quantization.quantize_fx import prepare_qat_fx
  from transformers import PreTrainedModel
  from transformers.utils.fx import HFTracer, check_if_model_is_supported, get_concrete_args, symbolic_trace
  from ..utils import check_if_available
label_desc: Use image node_id 1 to create a new QuantizationTracer object from inherited base classes: HFTracer with example: obj = QuantizationTracer(skipped_module_names, skipped_module_classes)