input: def build_cat_dog_classifier() -> keras.models.Sequential:
      """
      Build and compile a machine learning model using Keras that can distinguish between cats and dogs.
      The model is built with a series of convolutional and max pooling layers, followed by fully connected layers,
      and is compiled with binary cross-entropy loss and the Adam optimizer.
      The model is then trained using an ImageDataGenerator to process the input images.
      
      >>> model = build_cat_dog_classifier()
      >>> model.summary()
      """
      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
  
      # 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 and normalization
      train_datagen = ImageDataGenerator(rescale=1./255,
                                         shear_range=0.2,
                                         zoom_range=0.2,
                                         horizontal_flip=True)
  
      test_datagen = ImageDataGenerator(rescale=1./255)
  
      training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                       target_size=(64, 64),
                                                       batch_size=32,
                                                       class_mode='binary')
  
      test_set = test_datagen.flow_from_directory('dataset/test_set',
                                                  target_size=(64, 64),
                                                  batch_size=32,
                                                  class_mode='binary')
  
      # Train the model
      model.fit(training_set,
                steps_per_epoch=8000,
                epochs=25,
                validation_data=test_set,
                validation_steps=2000)