code: def test_dataframe_with_empty_partition(self):
      from zoo.orca import OrcaContext
  
      sc = OrcaContext.get_spark_context()
      rdd = sc.range(0, 10)
  
      rdd_with_empty = rdd.repartition(4).mapPartitionsWithIndex(
          lambda idx, part: [] if idx == 0 else part
      )
  
      from pyspark.sql import SparkSession
  
      spark = SparkSession(sc)
      from pyspark.ml.linalg import DenseVector
  
      df = rdd_with_empty.map(
          lambda x: (
              DenseVector(
                  np.random.randn(
                      1,
                  ).astype(np.float)
              ),
              int(np.random.randint(0, 1, size=())),
          )
      ).toDF(["feature", "label"])
  
      config = {"lr": 0.8}
      trainer = Estimator.from_keras(
          model_creator=model_creator,
          verbose=True,
          config=config,
          workers_per_node=2,
      )
  
      trainer.fit(
          df,
          epochs=1,
          batch_size=4,
          steps_per_epoch=25,
          feature_cols=["feature"],
          label_cols=["label"],
      )
      trainer.evaluate(
          df,
          batch_size=4,
          num_steps=25,
          feature_cols=["feature"],
          label_cols=["label"],
      )
      trainer.predict(df, feature_cols=["feature"]).collect()
  
imports: import shutil
  from unittest import TestCase
  import numpy
  import pytest
  import tensorflow
  from zoo import init_nncontext
  from zoo.orca.data import XShards
  import zoo.orca.data.pandas
  from zoo.orca.learn.tf2 import Estimator
  from zoo.ray import RayContext
  import ray
  import os
label_desc: Use image node_id 9 for calling the TestTFRayEstimator obj's underlying member method code with example usage: obj.test_dataframe_with_empty_partition() without return types