input: def create_cat_dog_classifier():
      """
      This function creates a machine learning model in Python using Keras to distinguish between cats and dogs.
      
      >>> model = create_cat_dog_classifier()
      >>> model.fit_generator(generator = train_generator, steps_per_epoch = 800, epochs = 5, validation_data = test_generator, validation_steps = 200)
      <keras.callbacks.History object at 0x...>
      """
output:     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=(64, 64, 3)))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
  
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      return model