def custom_model():
  from keras.models import Sequential
  from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
 
  model = Sequential()
  model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
  model.add(MaxPooling2D((2, 2)))
  model.add(Conv2D(64, (3, 3), activation='relu'))
  model.add(MaxPooling2D((2, 2)))
  model.add(Flatten())
  model.add(Dense(10, activation='softmax'))
  return model
 
print(custom_model())

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