input: Write a python function to classify a given image into one of the breed classes.
  assert classify(cv2.imread('dog1.jpg')) == 'Labrador'
  assert classify(cv2.imread('dog2.jpg')) == 'Beagle'
  assert classify(cv2.imread('dog3.jpg')) == 'Poodle'
output: def classify(image):
      import tensorflow as tf
      from tensorflow.keras.preprocessing import image as keras_image
      import numpy as np
      from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
  
      # Load the pre-trained ResNet50 model
      model = ResNet50(weights='imagenet')
  
      # Preprocess the image
      img = cv2.resize(image, (224, 224))
      img = keras_image.img_to_array(img)
      img = np.expand_dims(img, axis=0)
      img = preprocess_input(img)
  
      # Make predictions
      predictions = model.predict(img)
  
      # Decode the predictions
      decoded_predictions = decode_predictions(predictions, top=1)[0]
      breed = decoded_predictions[0][1]
  
      # Map common ImageNet breed names to specific breeds
      breed_mapping = {
          'Labrador_retriever': 'Labrador',
          'beagle': 'Beagle',
          'toy_poodle': 'Poodle',
          'miniature_poodle': 'Poodle',
          'standard_poodle': 'Poodle'
      }
  
      # Return the breed name based on mapping
      return breed_mapping.get(breed, 'Unknown')