input: 
  import numpy as np
  import keras
  from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
  from keras.models import Sequential
  from keras.preprocessing.image import ImageDataGenerator
  
  def create_cat_dog_classifier() -> Sequential:
      """Creates and returns a machine learning model that distinguishes between cats and dogs.
      The model is a Convolutional Neural Network (CNN) built using Keras.
      Example:
      model = create_cat_dog_classifier()
      """
  
      # Create the neural network model
      model = Sequential()
  
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
  
      # Hidden layers
      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))
  
      # Output layer
      model.add(Flatten())
      model.add(Dense(units = 128, activation = 'relu'))
      model.add(Dense(units = 1, activation = 'sigmoid'))
  
      # Compile the model
      model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
  
      return model
  
output:     # Data augmentation
      datagen = ImageDataGenerator(
          rescale=1./255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True)
  
      # Prepare the dataset
      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')
  
      # Train the model
      model.fit_generator(
          train_generator,
          steps_per_epoch=2000 // 32,
          epochs=50,
          validation_data=validation_generator,
          validation_steps=800 // 32)