class_docstr: 🤗 Constructor for the object detection trainer
code: def visualize_predictions(
      self, image, outputs, threshold=0.2, visualize=False
  ):
      # Get predictions probabilities
      probas = outputs.logits.softmax(-1)[0, :, :-1]
  
      # Keep only predictions with confidence >= threshold
      keep = probas.max(-1).values > threshold
  
      # Convert predicted boxes from [0; 1] to image scales
      bboxes_scaled = self.rescale_bboxes(
          outputs.pred_boxes[0, keep].cpu(), image.size
      )
  
      # plot results
      if visualize == True:
          self.plot_results(image, probas[keep], bboxes_scaled)
  
      return image, probas[keep], bboxes_scaled
  
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 5 for calling the ObjectDetectionInference obj's underlying member method code with example usage: obj.visualize_predictions(image, outputs, threshold, visualize) and returns: image,  probas,  bboxes_scaled