input: from typing import Tuple
  
  def create_cat_dog_classifier(dataset_path: str) -> Tuple[Sequential, ImageDataGenerator]:
      """Builds and compiles a neural network model that distinguishes between cats and dogs,
      using a dataset of images and an ImageDataGenerator for preprocessing.
      >>> model, datagen = create_cat_dog_classifier('/path/to/dataset')
      >>> model.summary()
      >>> datagen.flow_from_directory('/path/to/dataset')
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      from tensorflow.keras.preprocessing.image import ImageDataGenerator
  
      # Build the model
      model = Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
          MaxPooling2D(2, 2),
          Conv2D(64, (3, 3), activation='relu'),
          MaxPooling2D(2, 2),
          Conv2D(128, (3, 3), activation='relu'),
          MaxPooling2D(2, 2),
          Flatten(),
          Dense(512, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Create the ImageDataGenerator
      datagen = ImageDataGenerator(rescale=1./255)
  
      return model, datagen