code: def test_predict_xshards(self):
      train_data_shard = XShards.partition(
          {
              "x": np.random.randn(100, 1),
              "y": np.random.randint(0, 1, size=(100,)),
          }
      )
      expected = train_data_shard.collect()
  
      expected = [shard["x"] for shard in expected]
  
      for x in expected:
          print(x.shape)
  
      expected = np.concatenate(expected)
  
      config = {}
      trainer = Estimator.from_keras(
          model_creator=identity_model_creator,
          verbose=True,
          config=config,
          workers_per_node=2,
      )
  
      result_shards = trainer.predict(
          train_data_shard, batch_size=10
      ).collect()
  
      result = [shard["prediction"] for shard in result_shards]
      expected_result = [shard["x"] for shard in result_shards]
  
      result = np.concatenate(result)
  
      assert np.allclose(expected, result)
  
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 15 for calling the TestTF2EstimatorRayBackend obj's underlying member method code with example usage: obj.test_predict_xshards() without return types