code: def main():
      image_h = 224
      image_w = 224
      onnx_path = "resnet34.onnx"
      ir_path = "ir_output/resnet34.xml"
  
      image = np.random.randn(image_h, image_w, 3)
      normalized_image = normalize(image)
  
      # Convert the resized images to network input shape
      # [h, w, c] -> [c, h, w] -> [1, c, h, w]
      input_image = np.expand_dims(np.transpose(image, (2, 0, 1)), 0)
      normalized_input_image = np.expand_dims(
          np.transpose(normalized_image, (2, 0, 1)), 0
      )
  
      onnx_fps = onnx_inference(
          onnx_path, normalized_input_image, num_images=100
      )
      ir_fps = ir_inference(ir_path, input_image, num_images=100)
      pytorch_fps = pytorch_inference(
          normalized_input_image, num_images=100
      )
      plot_fps(
          {
              "pytorch": round(pytorch_fps, 2),
              "onnx": round(onnx_fps, 2),
              "ir": round(ir_fps, 2),
          }
      )
  
file_path: deep-learning-for-image-processing/deploying_service/deploying_pytorch/convert_openvino/convert_resnet34/compare_fps.py
imports: import time
  import numpy
  import torch
  import onnxruntime
  import matplotlib.pyplot
  from openvino.runtime import Core
  from torchvision.models import resnet34
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types