code: def _quantize_model(
      self,
      model: torch.nn.Module,
      quantization_type: QuantizationType,
      input_tfms: MultiStageTransformation,
      input_data_torch: List[Tuple[torch.Tensor, ...]],
  ):
      raise NotImplementedError()
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/torch_neuron.py
imports: from typing import List, Tuple
  from nebullvm.core.models import QuantizationType, ModelParams, DeviceType
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.operations.optimizations.compilers.quantizations.utils import check_quantization
  from nebullvm.optional_modules.torch import torch, symbolic_trace
  from nebullvm.optional_modules.torch_neuron import torch_neuron
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 4 for calling the TorchNeuronCompiler obj's underlying member method code with example usage: obj._quantize_model(model, quantization_type, input_tfms, input_data_torch) without return types