Question: Consider the cnn architecture given below: model.add ( layers . Conv 2 D ( 3 2 , ( 3 , 3 ) , activation =

Consider the cnn architecture given below:
model.add(layers.Conv2D(32,(3,3), activation="relu", input_shape=(28,28,1)))
cnnModel.add(layers.MaxPooling2D((2,2)))
cnnModel.add(layers.Dense(10, activation="softmax" ))
What will be the total no of trainable parameters in pooling layer?

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