input: 
  def build_cat_dog_model() -> keras.models.Sequential:
      """
      Build a simple Convolutional Neural Network using Keras to distinguish between images of cats and dogs.
      
      :return: A compiled Keras model ready to be trained.
      
      >>> model = build_cat_dog_model()
      >>> model.summary()
      ...
      Model: "sequential"
      _________________________________________________________________
      Layer (type)                 Output Shape              Param #   
      =================================================================
      conv2d (Conv2D)              (None, 62, 62, 32)        896       
      _________________________________________________________________
      max_pooling2d (MaxPooling2D) (None, 31, 31, 32)        0         
      _________________________________________________________________
      dropout (Dropout)            (None, 31, 31, 32)        0         
      _________________________________________________________________
      conv2d_1 (Conv2D)            (None, 29, 29, 64)        18496     
      _________________________________________________________________
      max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64)        0         
      _________________________________________________________________
      dropout_1 (Dropout)          (None, 14, 14, 64)        0         
      _________________________________________________________________
      conv2d_2 (Conv2D)            (None, 12, 12, 64)        36928     
      _________________________________________________________________
      max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         
      _________________________________________________________________
      dropout_2 (Dropout)          (None, 6, 6, 64)          0         
      _________________________________________________________________
      flatten (Flatten)            (None, 2304)              0         
      _________________________________________________________________
      dense (Dense)                (None, 128)               295040    
      _________________________________________________________________
      dense_1 (Dense)              (None, 1)                 129       
      =================================================================
      Total params: 351,489
      Trainable params: 351,489
      Non-trainable params: 0
      _________________________________________________________________
      """
      from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
      from keras.models import Sequential
  
      model = Sequential()
      model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Flatten())
      model.add(Dense(units=128, activation='relu'))
      model.add(Dense(units=1, activation='sigmoid'))
  
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      return model
  
output:     model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Flatten())
      model.add(Dense(units=128, activation='relu'))
      model.add(Dense(units=1, activation='sigmoid'))
  
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      return model