code: def compute_amax(model, **kwargs):
      # Load calib result
      for name, module in model.named_modules():
          if isinstance(module, quant_nn.TensorQuantizer):
              if module._calibrator is not None:
                  if isinstance(
                      module._calibrator, calib.MaxCalibrator
                  ):
                      module.load_calib_amax()
                  else:
                      module.load_calib_amax(**kwargs)
              print(f"{name:40}: {module}")
      model.cuda()
  
file_path: deep-learning-for-image-processing/deploying_service/deploying_pytorch/convert_tensorrt/convert_resnet34/quantization.py
imports: import os
  import math
  import argparse
  from absl import logging
  from tqdm import tqdm
  import torch
  import torch.optim
  import torch.optim.lr_scheduler
  from torchvision import transforms
  from torchvision.models.resnet import resnet34
  from pytorch_quantization import nn
  from pytorch_quantization import quant_modules, calib
  from pytorch_quantization.tensor_quant import QuantDescriptor
  from my_dataset import MyDataSet
  from utils import read_split_data, train_one_epoch, evaluate
label_desc: Use image node_id 3 for calling a global function with example usage: compute_amax(model) without return types