input: Write a python function to train and predict using DecisionTreeClassifier from scikit-learn.
  assert type(train_and_predict(X_train, y_train, X_test)) == np.ndarray
output: def train_and_predict(X_train, y_train, X_test):
      from sklearn.tree import DecisionTreeClassifier
      import numpy as np
  
      # Initialize the DecisionTreeClassifier
      clf = DecisionTreeClassifier()
  
      # Train the classifier with the training data
      clf.fit(X_train, y_train)
  
      # Predict the labels for the test data
      predictions = clf.predict(X_test)
  
      # Ensure the predictions are returned as a numpy array
      return np.array(predictions)