code: def specialized_quantization_tracer_creator(
      concrete_args: Dict[str, Any]
  ) -> Type:
      """Creates a QuantizationTracer-like class specifying concrete_args as a class attribute."""
      return type(
          "QuantizationTracer",
          (QuantizationTracer,),
          {"specialized_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 1 for calling a global function with example usage: specialized_quantization_tracer_creator(concrete_args) and returns: type