input: Write a python function to create and compile a sequential model using Keras.
  assert model.summary() is not None
  assert isinstance(model.layers[0], keras.layers.Dense)
  assert model.optimizer.__class__.__name__ == 'Adam'
output: def create_and_compile_model():
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.optimizers import Adam
  
      model = Sequential()
      model.add(Dense(32, input_dim=10, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
      
      model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
      
      return model
  
  model = create_and_compile_model()