class_docstr: Transformers compatible version of torch.quantization.quantize_fx.QuantizationTracer.
  This tracer is used internally to prepare the model for quantization.
code: def call_module(
      self,
      module: torch.nn.Module,
      forward: Callable[..., Any],
      args: Tuple[Any, ...],
      kwargs: Dict[str, Any],
  ) -> Any:
      module_qualified_name = self.path_of_module(module)
      # Creating scope with information of current module
      # scope will be restored automatically upon exit
      with ScopeContextManager(
          self.scope, module, module_qualified_name
      ):
          return super().call_module(module, forward, args, kwargs)
  
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 3 for calling the QuantizationTracer obj's underlying member method code with example usage: obj.call_module(module, forward, args, kwargs) and returns: super