input: 
  def identify_cats_and_dogs(model, train_generator, test_generator) -> keras.models.Sequential:
      """ Train a machine learning model to distinguish between cats and dogs using the given neural network model and data generators.
      >>> model = Sequential()
      >>> model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 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))
      >>> model.add(Conv2D(64, (3, 3), activation='relu'))
      >>> model.add(MaxPooling2D(pool_size=(2, 2)))
      >>> model.add(Dropout(0.3))
      >>> model.add(Flatten())
      >>> model.add(Dense(units=128, activation='relu'))
      >>> model.add(Dense(units=1, activation='sigmoid'))
      >>> model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
      >>> datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
      >>> train_generator = datagen.flow_from_directory(directory='/path/to/dataset', target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=32)
      >>> test_generator = datagen.flow_from_directory(directory='/path/to/dataset', target_size=(64, 64), color_mode='rgb', class_mode='binary', batch_size=32)
      >>> trained_model = identify_cats_and_dogs(model, train_generator, test_generator)
      >>> trained_model.history['accuracy'][-1]
      0.90
      """
      return model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=test_generator, validation_steps=200)
  
output:     
      model.fit_generator(generator=train_generator, steps_per_epoch=800, epochs=5, validation_data=test_generator, validation_steps=200)
      return model
  