in this image classification tutorial with transfer learning and fine-tuning ( Transfer learning and fine-tuning | TensorFlow Core](Transfer learning and fine-tuning  |  TensorFlow Core 1), when the model summary is called it shows all the layers, however, all the mobilenet layers are condensed into one layer which reads:  mobilenetv2_1.00_160 (Functi (None, 5, 5, 1280)
model.summary()

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 160, 160, 3)]     0         
_________________________________________________________________
sequential (Sequential)      (None, 160, 160, 3)       0         
_________________________________________________________________
tf.math.truediv (TFOpLambda) (None, 160, 160, 3)       0         
_________________________________________________________________
tf.math.subtract (TFOpLambda (None, 160, 160, 3)       0         
_________________________________________________________________
mobilenetv2_1.00_160 (Functi (None, 5, 5, 1280)        2257984   
_________________________________________________________________
global_average_pooling2d (Gl (None, 1280)              0         
_________________________________________________________________
dropout (Dropout)            (None, 1280)              0         
_________________________________________________________________
dense (Dense)                (None, 1)                 1281      
=================================================================
Total params: 2,259,265
Trainable params: 1,281
Non-trainable params: 2,257,984
_________________________________________________________________

why does it do this and how can i make it call a summary that shows all layers including the ones in the condensed mobilenet summary layer?
basically i need to read all convoluted layers for a project but it current not find any.