input: 
  def create_cat_dog_classifier():
      """
      Create a neural network model in Python using Keras that distinguishes between cats and dogs.
      The model should be built with convolutional and dense layers and trained on a dataset of 800 images.
      The function should return the compiled Keras model.
      
      Example:
      model = create_cat_dog_classifier()
      assert isinstance(model, keras.models.Sequential)
      assert model.layers[0].input_shape == (None, 64, 64, 3)
      assert model.layers[-1].output_shape == (None, 1)
      """
      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 preprocessing
      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 = 20, epochs = 10, validation_data = test_set, validation_steps = 20)