input: Write a python function to load a Caffe model, read an image, create a blob, make predictions, and draw bounding boxes for detected objects.
  assert detect_objects('model.prototxt', 'weights.caffemodel', 'image.jpg') is not None
output: import cv2
  import numpy as np
  
  def detect_objects(prototxt, model, image_path):
      # Load the Caffe model
      net = cv2.dnn.readNetFromCaffe(prototxt, model)
  
      # Read the image
      image = cv2.imread(image_path)
      (h, w) = image.shape[:2]
  
      # Create a blob from the image
      blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
  
      # Set the blob as input to the network
      net.setInput(blob)
  
      # Perform a forward pass to get the detections
      detections = net.forward()
  
      # Loop over the detections
      for i in range(detections.shape[2]):
          confidence = detections[0, 0, i, 2]
  
          if confidence > 0.2:
              idx = int(detections[0, 0, i, 1])
              box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
              (startX, startY, endX, endY) = box.astype("int")
  
              # Draw the bounding box around the detected object
              label = f"Object {idx}: {confidence:.2f}"
              cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
              y = startY - 15 if startY - 15 > 15 else startY + 15
              cv2.putText(image, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
  
      return image