code: def test_training_imageset(self):
      images = []
      labels = []
      for i in range(0, 32):
          features = np.random.uniform(0, 1, (200, 200, 3))
          label = np.array([2])
          images.append(features)
          labels.append(label)
      image_set = DistributedImageSet(
          self.sc.parallelize(images), self.sc.parallelize(labels)
      )
  
      transformer = ChainedPreprocessing(
          [
              ImageBytesToMat(),
              ImageResize(256, 256),
              ImageCenterCrop(224, 224),
              ImageChannelNormalize(
                  0.485, 0.456, 0.406, 0.229, 0.224, 0.225
              ),
              ImageMatToTensor(),
              ImageSetToSample(target_keys=["label"]),
          ]
      )
      data = image_set.transform(transformer)
  
      model = Sequential()
      model.add(Convolution2D(1, 5, 5, input_shape=(3, 224, 224)))
      model.add(Reshape((1 * 220 * 220,)))
      model.add(Dense(20, activation="softmax"))
      model.compile(
          optimizer="sgd",
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
      model.fit(data, batch_size=8, nb_epoch=2, validation_data=data)
      result = model.predict(data, batch_per_thread=8)
      accuracy = model.evaluate(data, batch_size=8)
  
imports: import pytest
  import shutil
  from pyspark.sql.types import *
  from pyspark.sql.functions import lit
  from bigdl.dllib.feature.common import ChainedPreprocessing
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.feature.image import *
  from bigdl.dllib.keras.layers import *
  from bigdl.dllib.keras.models import *
  from bigdl.dllib.nnframes import *
  from test.bigdl.test_zoo_utils import ZooTestCase
label_desc: Use image node_id 6 for calling the TestSimpleIntegration obj's underlying member method code with example usage: obj.test_training_imageset() without return types