code: def plot_fps(v: dict):
      x = list(v.keys())
      y = list(v.values())
  
      plt.bar(range(len(x)), y, align="center")
      plt.xticks(range(len(x)), x)
      for i, v in enumerate(y):
          plt.text(x=i, y=v + 0.5, s=f"{v:.2f}", ha="center")
      plt.xlabel("model format")
      plt.ylabel("fps")
      plt.title("FPS comparison")
      plt.show()
      plt.savefig("fps_vs.jpg")
  
file_path: deep-learning-for-image-processing/deploying_service/deploying_pytorch/convert_openvino/convert_yolov5/compare_fps.py
imports: import time
  import numpy
  import torch
  import onnxruntime
  import matplotlib.pyplot
  from openvino.runtime import Core
label_desc: Use image node_id 5 for calling a global function with example usage: plot_fps(v) without return types