code: def _quantize_model(
      self,
      model: Module,
      quantization_type: QuantizationType,
      input_tfms: MultiStageTransformation,
      input_data_torch: List[Tuple[torch.Tensor, ...]],
  ):
      return quantize_pytorch(
          model,
          quantization_type,
          input_tfms,
          input_data_torch,
          self.device,
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/torchscript.py
imports: from typing import Union, List, Tuple
  from nebullvm.config import QUANTIZATION_DATA_NUM
  from nebullvm.core.models import QuantizationType, DeviceType
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.operations.optimizations.compilers.quantizations.pytorch import quantize_pytorch
  from nebullvm.operations.optimizations.compilers.quantizations.utils import check_quantization
  from nebullvm.optional_modules.torch import torch, Module, ScriptModule, GraphModule, symbolic_trace
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 3 for calling the TorchScriptCompiler obj's underlying member method code with example usage: obj._quantize_model(model, quantization_type, input_tfms, input_data_torch) and returns: quantize_pytorch