code: def export_onnx(model, onnx_filename, onnx_bs):
      model.eval()
      # We have to shift to pytorch's fake quant ops before exporting the model to ONNX
      quant_nn.TensorQuantizer.use_fb_fake_quant = True
      opset_version = 13
  
      print(f"Export ONNX file: {onnx_filename}")
      dummy_input = torch.randn(onnx_bs, 3, 224, 224).cuda()
      torch.onnx.export(
          model,
          dummy_input,
          onnx_filename,
          verbose=False,
          opset_version=opset_version,
          enable_onnx_checker=False,
          input_names=["input"],
          output_names=["output"],
      )
  
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 1 for calling a global function with example usage: export_onnx(model, onnx_filename, onnx_bs) without return types