code: def test_training_with_tensorboard_checkpoint_gradientclipping(self):
      model = Sequential()
      model.add(
          Dense(
              8,
              input_shape=(
                  32,
                  32,
              ),
              name="dense1",
          )
      )
      model.add(Flatten())
      model.add(Dense(4, activation="softmax"))
      X_train = np.random.random([200, 32, 32])
      y_train = np.random.randint(4, size=(200,))
      X_test = np.random.random([40, 32, 32])
      y_test = np.random.randint(4, size=(40,))
      model.compile(
          optimizer="adam",
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
      tmp_log_dir = create_tmp_path()
      tmp_checkpoint_path = create_tmp_path()
      os.mkdir(tmp_checkpoint_path)
      model.set_tensorboard(tmp_log_dir, "training_test")
      model.set_checkpoint(tmp_checkpoint_path)
      model.set_constant_gradient_clipping(0.01, 0.03)
      model.freeze("dense1")
      model.fit(
          X_train,
          y_train,
          batch_size=112,
          nb_epoch=2,
          validation_data=(X_test, y_test),
      )
      model.clear_gradient_clipping()
      model.unfreeze("dense1")
      model.fit(
          X_train,
          y_train,
          batch_size=112,
          nb_epoch=2,
          validation_data=(X_test, y_test),
      )
      model.set_gradient_clipping_by_l2_norm(0.2)
      model.fit(
          X_train,
          y_train,
          batch_size=112,
          nb_epoch=2,
          validation_data=(X_test, y_test),
      )
      train_loss = model.get_train_summary("Loss")
      val_loss = model.get_validation_summary("Loss")
      np.array(train_loss)
      np.array(val_loss)
      eval = model.evaluate(X_test, y_test, batch_size=112)
      result = model.predict(X_test).collect()
      for res in result:
          assert isinstance(res, np.ndarray)
      result2 = model.predict(X_test, distributed=False)
      result_classes = model.predict_classes(X_test)
      shutil.rmtree(tmp_log_dir)
      shutil.rmtree(tmp_checkpoint_path)
  
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 3 for calling the TestSimpleIntegration obj's underlying member method code with example usage: obj.test_training_with_tensorboard_checkpoint_gradientclipping() without return types