input: 
  def create_cat_dog_classifier_model():
      """Create a machine learning model in Python that distinguishes between cats and dogs.
      The model uses a neural network built with Keras and TensorFlow. The model is trained
      on a dataset of 800 images of cats and dogs. The model includes convolutional layers,
      pooling layers, dropout layers, and fully connected layers. The model is compiled with
      'binary_crossentropy' loss, 'adam' optimizer, and 'accuracy' as the metric. Finally,
      the model is trained using an ImageDataGenerator for data augmentation and rescaling.
      >>> model = create_cat_dog_classifier_model()
      >>> model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=test_generator, validation_steps=200)
      """
  
output:     from keras.models import Sequential
      from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
      from keras.preprocessing.image import ImageDataGenerator
  
      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(Conv2D(128, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
      model.add(Flatten())
      model.add(Dense(512, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(1, activation='sigmoid'))
  
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
      test_datagen = ImageDataGenerator(rescale=1./255)
  
      train_generator = train_datagen.flow_from_directory('data/train', target_size=(64, 64), batch_size=32, class_mode='binary')
      test_generator = test_datagen.flow_from_directory('data/test', target_size=(64, 64), batch_size=32, class_mode='binary')
  
      return model