code: def test_dataframe_predict(self):
      sc = OrcaContext.get_spark_context()
      rdd = sc.parallelize(range(20))
      df = rdd.map(
          lambda x: (
              [float(x)] * 5,
              [int(np.random.randint(0, 2, size=()))],
          )
      ).toDF(["feature", "label"])
  
      estimator = Estimator.from_keras(
          model_creator=identity_model_creator,
          verbose=True,
          config={},
          workers_per_node=2,
      )
      result = estimator.predict(
          df, batch_size=4, feature_cols=["feature"]
      )
      expr = (
          "sum(cast(feature <> to_array(prediction) as int)) as error"
      )
      assert result.selectExpr(expr).first()["error"] == 0
  
imports: import math
  import os
  import shutil
  import tempfile
  import pytest
  from unittest import TestCase
  import tensorflow
  import numpy
  from bigdl.orca.data.pandas import read_csv
  from bigdl.orca.data import SparkXShards, XShards
  from bigdl.orca.data.tf.data import Dataset
  from bigdl.orca.learn.tf2 import Estimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca import OrcaContext
label_desc: Use image node_id 13 for calling the TestTF2EstimatorRayBackend obj's underlying member method code with example usage: obj.test_dataframe_predict() without return types