input: Write a python function to create and train a Decision Tree classifier, then make predictions and return them.
  assert (create_and_train_decision_tree(X, y) == clf.predict(X)).all()
  assert len(create_and_train_decision_tree(X, y)) == len(y)
  assert isinstance(create_and_train_decision_tree(X, y), np.ndarray)
output: def create_and_train_decision_tree(X, y):
      from sklearn.tree import DecisionTreeClassifier
      import numpy as np
      
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      predictions = clf.predict(X)
      
      return predictions