input: Write a python function to initialize, train, and evaluate an MLPClassifier model using scikit-learn, given input features and target labels.
  assert train_and_evaluate_mlp(X, y) > 0.7
  assert isinstance(train_and_evaluate_mlp(X, y), float)
output: def train_and_evaluate_mlp(X, y):
      from sklearn.neural_network import MLPClassifier
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      mlp = MLPClassifier(random_state=42)
      mlp.fit(X_train, y_train)
      y_pred = mlp.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy