code: def pytorch_model_speed(data_loader, val_num):
      net = resnet34(num_classes=5)
      # load weights
      model_weight_path = "./resNet34.pth"
      check_path_exist(model_weight_path)
      net.load_state_dict(
          torch.load(model_weight_path, map_location=device),
          strict=False,
      )
      net.eval()
      test_data = torch.rand((1, 3, 224, 224))
      net(test_data.to(device))
  
      forward_time = 0
      acc = 0.0  # accumulate accurate number / epoch
      with torch.no_grad():
          for val_data in tqdm(
              data_loader, desc="Running pytorch model..."
          ):
              val_images, val_labels = val_data
              t1 = time.time()
              outputs = net(
                  val_images.to(device)
              )  # eval model only have last output layer
              t2 = time.time()
              forward_time += t2 - t1
              predict_y = torch.max(outputs, dim=1)[1]
              acc += (predict_y == val_labels.to(device)).sum().item()
          val_accurate = acc / val_num
      fps = round(val_num / forward_time, 1)
      print(
          "pytorch info:\nfps: {}/s  accuracy: {}\n".format(
              fps, val_accurate
          )
      )
      return fps, val_accurate, "Pytorch(not opt)"
  
file_path: deep-learning-for-image-processing/others_project/openvinotest/openvino_cls_test/speed_test.py
imports: import os
  import time
  import torch
  from torchvision import transforms, datasets
  from tqdm import tqdm
  import onnx
  import onnxruntime
  import numpy
  from openvino.inference_engine import IECore
  from model import resnet34
label_desc: Use image node_id 3 for calling a global function with example usage: pytorch_model_speed(data_loader, val_num) and returns: fps,  val_accurate,  str