input: def classify_cats_and_dogs(model: Sequential, datagen: ImageDataGenerator) -> Sequential:
      """
      Collate a machine learning model in Python that distinguishes between cats and dogs using a given model and data generator.
      >>> 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'])
      >>> datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
      >>> classify_cats_and_dogs(model, datagen)
      <keras.engine.sequential.Sequential object at 0x...>
      """
output:     train_generator = datagen.flow_from_directory(
          'data/train',
          target_size=(64, 64),
          batch_size=32,
          class_mode='binary'
      )
  
      validation_generator = datagen.flow_from_directory(
          'data/validation',
          target_size=(64, 64),
          batch_size=32,
          class_mode='binary'
      )
  
      model.fit(
          train_generator,
          steps_per_epoch=2000 // 32,
          epochs=10,
          validation_data=validation_generator,
          validation_steps=800 // 32
      )
  
      return model