code: def test_training_with_dataframe_image(self):
      self.sc.stop()
      sparkConf = init_spark_conf().setMaster("local[1]")
      self.sc = init_nncontext(sparkConf)
  
      resource_path = os.path.join(
          os.path.split(__file__)[0], "../resources"
      )
      image_path = os.path.join(resource_path, "gray/gray.bmp")
      image_df = NNImageReader.readImages(
          image_path, self.sc
      ).withColumn("label", lit(1))
  
      from bigdl.dllib.feature.image import transforms
  
      transformers = transforms.Compose(
          [ImageResize(50, 50), ImageMirror()]
      )
      model = Sequential()
      model.add(
          Convolution2D(
              1, 24, 24, activation="relu", input_shape=(1, 50, 50)
          )
      )
      model.add(MaxPooling2D())
      model.add(Reshape([169]))
      model.add(Dense(2, activation="log_softmax"))
      model.compile(
          optimizer="sgd", loss="sparse_categorical_crossentropy"
      )
      model.fit(
          image_df,
          label_cols=["label"],
          batch_size=1,
          nb_epoch=1,
          transform=transformers,
      )
  
      predDf = model.predict(
          image_df, prediction_col="predict", transform=transformers
      )
      predDf.show()
      model.evaluate(
          image_df,
          batch_size=1,
          label_cols=["label"],
          transform=transformers,
      )
  
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 20 for calling the TestSimpleIntegration obj's underlying member method code with example usage: obj.test_training_with_dataframe_image() without return types