code: def _quantize_model(
      self,
      model_path: str,
      input_data: List[Tuple[np.ndarray, ...]],
      quantization_type: QuantizationType,
      input_tfms: MultiStageTransformation,
  ):
      return quantize_onnx(
          model_path,
          input_data,
          quantization_type,
          self.device,
          input_tfms,
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/onnxruntime.py
imports: from pathlib import Path
  from typing import Union, List, Tuple
  import numpy
  from nebullvm.config import QUANTIZATION_DATA_NUM
  from nebullvm.core.models import QuantizationType
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.operations.optimizations.compilers.quantizations.onnx import quantize_onnx
  from nebullvm.operations.optimizations.compilers.quantizations.utils import check_quantization
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 3 for calling the ONNXCompiler obj's underlying member method code with example usage: obj._quantize_model(model_path, input_data, quantization_type, input_tfms) and returns: quantize_onnx