code: def _get_quantization_type_for_static(
      use_gpu,
  ) -> Tuple[QuantType, QuantType]:
      """Returns the quantization types for activations and weights,
      depending on the underlying hardware
      """
      arch = cpuinfo.get_cpu_info()["arch"].lower()
      if use_gpu:
          activation_type = weight_type = QuantType.QInt8
      elif "x86" in arch:
          cpu_raw_data = cpuinfo.get_cpu_info()["brand_raw"].lower()
          if "intel" in cpu_raw_data and "xeon" in cpu_raw_data:
              activation_type = QuantType.QUInt8
              weight_type = QuantType.QInt8
          else:
              activation_type = weight_type = QuantType.QUInt8
      else:
          activation_type = QuantType.QUInt8
          weight_type = QuantType.QUInt8
      return activation_type, weight_type
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/quantizations/onnx.py
imports: from pathlib import Path
  from typing import Union, Iterable, Tuple, List
  import cpuinfo
  import numpy
  from nebullvm.core.models import QuantizationType, Device, DeviceType
  from nebullvm.optional_modules.onnx import onnx, convert_float_to_float16_model_path
  from nebullvm.optional_modules.onnxruntime import CalibrationDataReader, QuantType, quantize_dynamic, quantize_static
  from nebullvm.optional_modules.torch import DataLoader
  from nebullvm.tools.onnx import get_input_names
  from nebullvm.tools.transformations import MultiStageTransformation, HalfPrecisionTransformation
label_desc: Use image node_id 2 for calling a global function with example usage: _get_quantization_type_for_static(use_gpu) and returns: activation_type,  weight_type