How can I use tf.keras.Model.summary to see the layers of a child model which in a father model?

In order to be able to view backbone's layers, you' ll have to construct your new model using backbone.input and backbone.output

from tensorflow.keras.models import Model
def  Mymodel(backbone_model, classes):
    backbone = backbone_model
    x = backbone.output
    x = tf.keras.layers.Dense(classes,activation='sigmoid')(x)
    model = Model(inputs=backbone.input, outputs=x)
    return model

input_shape = (224, 224, 3)
model = Mymodel(backbone_model=tf.keras.applications.MobileNet(input_shape=input_shape, include_top=False, pooling='avg'),
                classes=61)

model.summary()

@Ioannis 's answer is perfectly fine, but unfortunately it drops the keras 'Model Subclassing' structure that is present in the question. If, just like me, you want to keep this model subclassing and still show all layers in the summary, you can branch down into all the individual layers of the more complex model using a for loop:

class MyMobileNet(tf.keras.Sequential):
    def __init__(self, input_shape=(224, 224, 3), classes=61):
        super(MyMobileNet, self).__init__()
        self.backbone_model = [layer for layer in
               tf.keras.applications.MobileNet(input_shape, include_top=False, pooling='avg').layers]
        self.classificator = tf.keras.layers.Dense(classes,activation='sigmoid', name='classificator')

    def call(self, inputs):
        x = inputs
        for layer in self.backbone_model:
            x = layer(x)
        x = self.classificator(x)
        return x
model = MyMobileNet()

After this we can directly build the model and call the summary:

model.build(input_shape=(None, 224, 224, 3))
model.summary()

>
Model: "my_mobile_net"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         
_________________________________________________________________
conv1 (Conv2D)               (None, 112, 112, 32)      864       
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 32)      128       
_________________________________________________________________
....
....
conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   
_________________________________________________________________
conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      
_________________________________________________________________
conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         
_________________________________________________________________
global_average_pooling2d_13  (None, 1024)              0         
_________________________________________________________________
classificator (Dense)        multiple                  62525     
=================================================================
Total params: 3,291,389
Trainable params: 3,269,501
Non-trainable params: 21,888
_________________________________________________________________