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 train_cat_dog_classifier(dataset_path: str) -> Sequential:
      """Train a machine learning model that distinguishes between cats and dogs using a given dataset.
      The dataset should be organized in directories where each directory contains images of one class.
      The images should be of size 64x64.
      
      >>> model = train_cat_dog_classifier('/path/to/dataset')
      >>> model.summary()
      Model: "sequential"
      _________________________________________________________________
      Layer (type)                 Output Shape              Param #   
      =================================================================
      conv2d (Conv2D)              (None, 62, 62, 32)        896       
      _________________________________________________________________
      max_pooling2d (MaxPooling2D) (None, 31, 31, 32)        0         
      _________________________________________________________________
      dropout (Dropout)            (None, 31, 31, 32)        0         
      _________________________________________________________________
      conv2d_1 (Conv2D)            (None, 29, 29, 64)        18496     
      _________________________________________________________________
      max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64)        0         
      _________________________________________________________________
      dropout_1 (Dropout)          (None, 14, 14, 64)        0         
      _________________________________________________________________
      conv2d_2 (Conv2D)            (None, 12, 12, 64)        36928     
      _________________________________________________________________
      max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         
      _________________________________________________________________
      dropout_2 (Dropout)          (None, 6, 6, 64)          0         
      _________________________________________________________________
      flatten (Flatten)            (None, 2304)              0         
      _________________________________________________________________
      dense (Dense)                (None, 128)               295040    
      _________________________________________________________________
      dense_1 (Dense)              (None, 1)                 129       
      =================================================================
      Total params: 351,489
      Trainable params: 351,489
      Non-trainable params: 0
      _________________________________________________________________
      """
      
      # 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 = dataset_path, 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)
      
      return model
output:     # Create data generator for testing
      test_datagen = ImageDataGenerator(rescale = 1./255)
      test_generator = test_datagen.flow_from_directory(directory = dataset_path, target_size = (64, 64), color_mode = 'rgb', class_mode = 'binary', batch_size = 32)