hey, I need to build an autoencoder network that has a bottleneck with 9 neurons in it, but I can't tell how many neurons are in each network. How would I go about understanding this? I need to make an autoencoder based off this code.

model=keras.models.Sequential()

# 3x3 kernel size, 10 channels in first hidden layer:

model.add(keras.layers.Conv2D(4,5,input_shape=(None,None,1),

activation="sigmoid",padding='same'))

model.add(keras.layers.AveragePooling2D(pool_size=(3,3),padding='same')) # down

model.add(keras.layers.Conv2D(4,5,

activation="sigmoid",padding='same'))

model.add(keras.layers.AveragePooling2D(pool_size=(3,3),padding='same')) # down

model.add(keras.layers.Conv2D(1,3,

activation="sigmoid",padding='same'))

model.add(keras.layers.UpSampling2D(size=(3,3))) # up

model.add(keras.layers.Conv2D(4,5,

activation="sigmoid",padding='same'))

model.add(keras.layers.UpSampling2D(size=(3,3))) # up

model.add(keras.layers.Conv2D(4,5,

activation="sigmoid",padding='same'))

model.add(keras.layers.Conv2D(1,3,activation="linear",padding='same'))

model.compile(loss='mean_squared_error',

optimizer='adam')

model.summary()