class_docstr: Transformers compatible version of torch.quantization.quantize_fx.QuantizationTracer.
  This tracer is used internally to prepare the model for quantization.
code: def trace(
      self,
      root: "PreTrainedModel",
      concrete_args: Optional[Dict[str, Any]] = None,
  ) -> "Graph":
      if (
          concrete_args is None
          and self.specialized_concrete_args is not None
      ):
          concrete_args = self.specialized_concrete_args
      return super().trace(root, concrete_args=concrete_args)
  
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 5 for calling the QuantizationTracer obj's underlying member method code with example usage: obj.trace(root, concrete_args) and returns: super