code: def _quantize_dynamic_torch_fx(
      model: GraphModule,
      input_data: List[Tuple[torch.Tensor, ...]],
  ):
      qconfig_dict = {"": default_dynamic_qconfig}
  
      additional_arguments = {}
      if check_module_version(torch, min_version="1.13.0"):
          additional_arguments["example_inputs"] = input_data[0]
  
      model_prepared = prepare_fx(
          model, qconfig_dict, **additional_arguments
      )
      return convert_fx(model_prepared)
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/quantizations/pytorch.py
imports: import copy
  from typing import List, Tuple, Union
  from loguru import logger
  from nebullvm.core.models import DeviceType, Device, QuantizationType
  from nebullvm.optional_modules.torch import torch, Module, symbolic_trace, QuantStub, DeQuantStub, GraphModule, default_dynamic_qconfig, prepare_fx, convert_fx, ScriptModule
  from nebullvm.tools.transformations import MultiStageTransformation, HalfPrecisionTransformation
  from nebullvm.tools.utils import check_module_version
label_desc: Use image node_id 2 for calling a global function with example usage: _quantize_dynamic_torch_fx(model, input_data) and returns: convert_fx