input: Write a python function to create and train a decision tree classifier using the sklearn library.
  assert isinstance(create_and_train_decision_tree(pd.DataFrame({"Age":[25, 45], "Income":[50000, 100000], "Gender":[0, 1], "Credit":[1, 0]})), tree.DecisionTreeClassifier)
  assert create_and_train_decision_tree(pd.DataFrame({"Age":[25, 45], "Income":[50000, 100000], "Gender":[0, 1], "Credit":[1, 0]})).predict([[25, 50000, 0]])==[1]
  assert create_and_train_decision_tree(pd.DataFrame({"Age":[25, 45], "Income":[50000, 100000], "Gender":[0, 1], "Credit":[1, 0]})).predict([[45, 100000, 1]])==[0]
output: def create_and_train_decision_tree(df):
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
  
      X = df.drop(columns=['Credit'])
      y = df['Credit']
  
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
  
      return clf