请考虑以下模型

def create_model():
  x_1=tf.Variable(24)
  bias_initializer = tf.keras.initializers.HeNormal()
  model = Sequential()
  model.add(Conv2D(64, (5, 5),  input_shape=(28,28,1),activation="relu", name='conv2d_1', use_bias=True,bias_initializer=bias_initializer))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Conv2D(32, (5, 5), activation="relu",name='conv2d_2',  use_bias=True,bias_initializer=bias_initializer))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Flatten())
  model.add(Dense(120, name='dense_1',activation="relu", use_bias=True,bias_initializer=bias_initializer),)
  model.add(Dense(10, name='dense_2', activation="softmax", use_bias=True,bias_initializer=bias_initializer),)

我可以提取模型示例的摘要，但是否有任何方法可以给予/计算层数（带有可训练参数）？例如，上面发布的模型有4个带有可训练参数的层。