class_docstr: Transformers compatible version of torch.quantization.quantize_fx.QuantizationTracer.
  This tracer is used internally to prepare the model for quantization.
code: def is_leaf_module(
      self, module: torch.nn.Module, module_qualified_name: str
  ) -> bool:
      return (
          (
              module.__module__.startswith("torch.nn")
              and not isinstance(module, torch.nn.Sequential)
          )
          or module_qualified_name in self.skipped_module_names
          or type(module) in self.skipped_module_classes
          or isinstance(module, _FusedModule)
          or super().is_leaf_module(module, module_qualified_name)
      )
  
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 2 for calling the QuantizationTracer obj's underlying member method code with example usage: obj.is_leaf_module(module, module_qualified_name) and returns: unknown