code: def test_sparkxshards(self):
      train_data_shard = XShards.partition(
          {
              "x": np.random.randn(100, 1),
              "y": np.random.randint(0, 1, size=(100)),
          }
      ).repartition(8)
  
      config = {"lr": 0.8}
      trainer = Estimator.from_keras(
          model_creator=model_creator,
          verbose=True,
          config=config,
          workers_per_node=2,
      )
  
      stats = trainer.fit(
          train_data_shard, epochs=1, batch_size=4, steps_per_epoch=25
      )
      assert isinstance(stats, dict), "fit should return a dict"
      stats = trainer.evaluate(
          train_data_shard, batch_size=4, num_steps=25
      )
      assert isinstance(stats, dict), "evaluate should return a dict"
  
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 4 for calling the TestTF2EstimatorRayBackend obj's underlying member method code with example usage: obj.test_sparkxshards() without return types