input: Write a python function to train a RandomForestClassifier and predict using the provided training and test data.
  assert np.array_equal(train_and_predict_rf(X_train, y_train, X_test), y_pred)
  assert train_and_predict_rf([[1, 2], [3, 4]], [0, 1], [[1, 2]]) in [0, 1]
  assert isinstance(train_and_predict_rf(X_train, y_train, X_test), np.ndarray)
output: def train_and_predict_rf(X_train, y_train, X_test):
      from sklearn.ensemble import RandomForestClassifier
      import numpy as np
  
      # Instantiate the model
      rf = RandomForestClassifier()
  
      # Train the model
      rf.fit(X_train, y_train)
  
      # Predict on the test data
      y_pred = rf.predict(X_test)
      
      return y_pred