code: def create_train_features_Set(self):
      image_set = self.get_raw_image_set(with_label=True)
      feature_set = FeatureSet.image_frame(image_set.to_image_frame())
      train_transformer = ChainedPreprocessing(
          [
              ImageBytesToMat(),
              ImageResize(256, 256),
              ImageRandomCrop(224, 224),
              ImageRandomPreprocessing(ImageHFlip(), 0.5),
              ImageChannelNormalize(
                  0.485, 0.456, 0.406, 0.229, 0.224, 0.225
              ),
              ImageMatToTensor(to_RGB=True, format="NHWC"),
              ImageSetToSample(
                  input_keys=["imageTensor"], target_keys=["label"]
              ),
          ]
      )
      feature_set = feature_set.transform(train_transformer)
      feature_set = feature_set.transform(ImageFeatureToSample())
      return feature_set
  
imports: import pytest
  from pyspark.ml.linalg import DenseVector
  from zoo.feature.common import ChainedPreprocessing, FeatureSet
  from zoo.feature.image import *
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.pipeline.api.keras.optimizers import Adam
  from zoo.tfpark import TFNet, TFOptimizer
  import tensorflow
  import numpy
  import os
  from zoo.tfpark import KerasModel, TFDataset
label_desc: Use image node_id 7 for calling the TestTFDataset obj's underlying member method code with example usage: obj.create_train_features_Set() and returns: feature_set