code: def test_nnEstimator_fit_gradient_clipping(self):
      model = Sequential().add(Linear(2, 2))
      criterion = MSECriterion()
      estimator = (
          NNEstimator(
              model, criterion, SeqToTensor([2]), ArrayToTensor([2])
          )
          .setBatchSize(4)
          .setLearningRate(0.2)
          .setMaxEpoch(2)
          .setConstantGradientClipping(0.1, 0.2)
      )
  
      df = self.get_estimator_df()
      estimator.fit(df)
      estimator.clearGradientClipping()
      estimator.fit(df)
      estimator.setGradientClippingByL2Norm(1.2)
      estimator.fit(df)
  
imports: import errno
  import shutil
  import pytest
  from bigdl.dllib.nn.criterion import *
  from bigdl.dllib.nn.layer import *
  from bigdl.dllib.optim.optimizer import *
  from numpy.testing import assert_allclose
  from pyspark.ml import Pipeline, PipelineModel
  from pyspark.ml.feature import MinMaxScaler
  from pyspark.ml.tuning import ParamGridBuilder
  from pyspark.sql.types import *
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.feature.common import *
  from bigdl.dllib.feature.image import *
  from bigdl.dllib.keras import layers
  from bigdl.dllib.keras.models import Model
  from bigdl.dllib.keras.optimizers import Adam
  from bigdl.dllib.nnframes import *
  from bigdl.dllib.utils.tf import *
  from pyspark.sql.functions import array
  from pyspark.ml.linalg import DenseVector, VectorUDT
  from pyspark.sql.functions import udf, array
label_desc: Use image node_id 12 for calling the TestNNClassifer obj's underlying member method code with example usage: obj.test_nnEstimator_fit_gradient_clipping() without return types