code: def test_estimator_keras_xshards_disk_featureset_trigger(self):
      import bigdl.orca.data.pandas
  
      tf.reset_default_graph()
  
      model = self.create_model()
      file_path = os.path.join(self.resource_path, "orca/learn/ncf.csv")
      data_shard = bigdl.orca.data.pandas.read_csv(file_path)
  
      def transform(df):
          result = {
              "x": (
                  df["user"].to_numpy().reshape([-1, 1]),
                  df["item"].to_numpy().reshape([-1, 1]),
              ),
              "y": df["label"].to_numpy(),
          }
          return result
  
      data_shard = data_shard.transform_shard(transform)
      from bigdl.dllib.optim.optimizer import SeveralIteration
      from bigdl.dllib.utils.triggers import (
          SeveralIteration as ZSeveralIteration,
      )
      from bigdl.dllib.utils.triggers import MinLoss as ZMinLoss
      from bigdl.dllib.utils.triggers import TriggerAnd as ZTriggerAnd
  
      est = Estimator.from_keras(keras_model=model)
      OrcaContext.train_data_store = "DISK_2"
      with self.assertRaises(Exception) as context:
          est.fit(
              data=data_shard,
              batch_size=4,
              epochs=10,
              validation_data=data_shard,
              checkpoint_trigger=SeveralIteration(2),
          )
      self.assertTrue(
          "Please use a trigger defined in bigdl.dllib.utils.triggers"
          in str(context.exception)
      )
  
      est.fit(
          data=data_shard,
          batch_size=4,
          epochs=10,
          validation_data=data_shard,
          checkpoint_trigger=ZTriggerAnd(
              ZSeveralIteration(2), ZMinLoss(0.2)
          ),
      )
      OrcaContext.train_data_store = "DRAM"
  
imports: import shutil
  from unittest import TestCase
  import tensorflow
  from bigdl.orca import OrcaContext
  from bigdl.orca.learn.trigger import SeveralIteration
  from bigdl.orca.learn.tf.estimator import Estimator
  from bigdl.dllib.nncontext import *
  from bigdl.orca.learn.utils import convert_predict_rdd_to_dataframe
  from bigdl.orca.learn.tf.estimator import save_model_dir
label_desc: Use image node_id 20 for calling the TestEstimatorForKeras obj's underlying member method code with example usage: obj.test_estimator_keras_xshards_disk_featureset_trigger() and returns: result