input: Write a python function to build, compile, and train a simple neural network model using TensorFlow and Keras.
  assert isinstance(build_and_train_model(X_train, y_train, X_test, y_test), tf.keras.Sequential)
  assert build_and_train_model(X_train, y_train, X_test, y_test).evaluate(X_test, y_test, verbose=2)[1] > 0.5
output: import tensorflow as tf
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def build_and_train_model(X_train, y_train, X_test, y_test):
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(64, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
  
      model.compile(optimizer='adam',
                    loss='binary_crossentropy',
                    metrics=['accuracy'])
  
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
  
      return model