code: def test_gradient_clipping(self):
      data = tf.keras.layers.Input(shape=[10])
  
      x = tf.keras.layers.Flatten()(data)
      x = tf.keras.layers.Dense(10, activation="relu")(x)
      predictions = tf.keras.layers.Dense(2, activation="softmax")(x)
  
      model = tf.keras.models.Model(inputs=data, outputs=predictions)
      model.compile(
          optimizer=tf.keras.optimizers.SGD(lr=1, clipvalue=1e-8),
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
      model = KerasModel(model)
  
      pre_weights = model.get_weights()
  
      dataset = self.create_training_dataset()
  
      # 5 iterations
      model.fit(dataset)
  
      current_weight = model.get_weights()
  
      np.all(np.abs((current_weight[0] - pre_weights[0])) < 1e-7)
  
imports: import pytest
  from pyspark.ml.linalg import DenseVector
  from bigdl.dllib.feature.common import ChainedPreprocessing, FeatureSet
  from bigdl.dllib.feature.image import *
  from bigdl.orca.test_zoo_utils import ZooTestCase
  from bigdl.dllib.keras.optimizers import Adam
  from bigdl.orca.tfpark import TFNet, TFOptimizer
  import tensorflow
  import numpy
  import os
  from bigdl.orca.tfpark import KerasModel, TFDataset
label_desc: Use image node_id 13 for calling the TestTFDataset obj's underlying member method code with example usage: obj.test_gradient_clipping() without return types