class_docstr: 🤗 Constructor for the object detection trainer
code: def plot_results(self, pil_img, prob, boxes):
      plt.figure(figsize=(16, 10))
      plt.imshow(pil_img)
      ax = plt.gca()
  
      colors = self.COLORS * 100
  
      # For each bbox
      for p, (xmin, ymin, xmax, ymax), c in zip(
          prob, boxes.tolist(), colors
      ):
          # Draw the bbox as a rectangle
          ax.add_patch(
              plt.Rectangle(
                  (xmin, ymin),
                  xmax - xmin,
                  ymax - ymin,
                  fill=False,
                  color=c,
                  linewidth=3,
              )
          )
  
          # Get the highest probability
          cl = p.argmax()
  
          # Draw the label
          text = f"{self.model.model.config.id2label[cl.item()]}: {p[cl]:0.2f}"
          ax.text(
              xmin,
              ymin,
              text,
              fontsize=15,
              bbox=dict(facecolor="yellow", alpha=0.5),
          )
  
      plt.axis("off")
  
      IMG_OUT = "./out_img/"
  
      if not os.path.exists(IMG_OUT):
          os.makedirs(IMG_OUT)
  
      plt.savefig(
          IMG_OUT
          + datetime.today().strftime("%Y-%m-%d-%H-%M-%S")
          + ".jpg"
      )
  
file_path: HugsVision/hugsvision/inference/ObjectDetectionInference.py
imports: import os
  from datetime import datetime
  import torch
  from transformers import DetrFeatureExtractor, DetrForObjectDetection, pipeline
  from PIL import Image
  import matplotlib
  import matplotlib.pyplot
label_desc: Use image node_id 4 for calling the ObjectDetectionInference obj's underlying member method code with example usage: obj.plot_results(pil_img, prob, boxes) without return types