input: def distinguish_cats_and_dogs(dataset_dir: str, target_size: tuple, color_mode: str, class_mode: str, batch_size: int) -> Sequential:
      """ Collate a machine learning model in Python that distinguishes between cats and dogs.
      >>> distinguish_cats_and_dogs('/path/to/dataset', (64, 64), 'rgb', 'binary', 32)
      <keras.engine.sequential.Sequential object at 0x...>
      """
output:     from tensorflow.keras.preprocessing.image import ImageDataGenerator
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
  
      # Data preprocessing
      datagen = ImageDataGenerator(rescale=1./255)
      train_generator = datagen.flow_from_directory(
          dataset_dir,
          target_size=target_size,
          color_mode=color_mode,
          class_mode=class_mode,
          batch_size=batch_size
      )
  
      # Model architecture
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(target_size[0], target_size[1], 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'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      return model