class_docstr: Transformers compatible version of torch.quantization.quantize_fx.QuantizationTracer.
  This tracer is used internally to prepare the model for quantization.
code: def create_node(
      self,
      kind: str,
      target: Target,
      args: Tuple[Argument, ...],
      kwargs: Dict[str, Argument],
      name: Optional[str] = None,
      type_expr: Optional[Any] = None,
  ) -> Node:
      node = super().create_node(
          kind, target, args, kwargs, name, type_expr
      )
      self.node_name_to_scope[node.name] = (
          self.scope.module_path,
          self.scope.module_type,
      )
      return node
  
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 4 for calling the QuantizationTracer obj's underlying member method code with example usage: obj.create_node(kind, target, args, kwargs, name, type_expr) and returns: node