input: 
  def build_cat_dog_classifier() -> None:
      """ Build a machine learning model in Python that distinguishes between cats and dogs using Keras. 
      The model should be trained on a dataset of 800 images of cats and dogs. The model architecture 
      includes convolutional layers, max pooling, dropout, and fully connected layers. The model is 
      compiled using binary crossentropy loss and the Adam optimizer.
      >>> build_cat_dog_classifier()
       Model successfully compiled and trained on dataset. 
      """
      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'])
  
      # Create data generator
      datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)
  
      # Fit the model
      train_generator = datagen.flow_from_directory(directory = '/path/to/dataset', target_size = (64, 64), color_mode = 'rgb', class_mode = 'binary', batch_size = 32)
      model.fit_generator(generator = train_generator, steps_per_epoch = 800, epochs = 5, validation_data = test_generator, validation_steps = 200)
  
output:     print("Model successfully compiled and trained on dataset.")