input: Write a python function to create and train a Keras Sequential model for binary classification.
  assert create_and_train_model(x_train, y_train).evaluate(x_test, y_test)[1] > 0.5
output: def create_and_train_model(x_train, y_train):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      model = Sequential()
      model.add(Dense(16, input_dim=x_train.shape[1], activation='relu'))
      model.add(Dense(8, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
  
      model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
      
      model.fit(x_train, y_train, epochs=10, batch_size=32, verbose=0)
      
      return model