input: def create_cat_dog_classifier(dataset_path: str) -> keras.models.Sequential:
      """ Create a machine learning model in Python that distinguishes between cats and dogs using a dataset of images.
      The function expects the path to the dataset and returns a compiled keras model.
      >>> model = create_cat_dog_classifier('/path/to/dataset')
      >>> isinstance(model, keras.models.Sequential)
      True
      >>> model.input_shape
      (None, 64, 64, 3)
      >>> model.output_shape
      (None, 1)
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      from tensorflow.keras.preprocessing.image import ImageDataGenerator
  
      # Define the model
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Data augmentation and preprocessing
      train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)
  
      train_generator = train_datagen.flow_from_directory(
          dataset_path,
          target_size=(64, 64),
          batch_size=32,
          class_mode='binary',
          subset='training'
      )
  
      validation_generator = train_datagen.flow_from_directory(
          dataset_path,
          target_size=(64, 64),
          batch_size=32,
          class_mode='binary',
          subset='validation'
      )
  
      # Train the model
      model.fit(
          train_generator,
          steps_per_epoch=train_generator.samples // train_generator.batch_size,
          validation_data=validation_generator,
          validation_steps=validation_generator.samples // validation_generator.batch_size
      )
  
      return model