code: def test_xshards_predict(self):
      sc = init_nncontext()
      rdd = sc.range(0, 110).map(lambda x: np.array([x] * 50))
      shards = rdd.mapPartitions(lambda iter: chunks(iter, 5)).map(
          lambda x: {"x": np.stack(x)}
      )
      shards = SparkXShards(shards)
  
      estimator = get_estimator(
          workers_per_node=2, model_fn=lambda config: IdentityNet()
      )
      result_shards = estimator.predict(shards, batch_size=4)
      result = np.concatenate(
          [shard["prediction"] for shard in result_shards.collect()]
      )
      expected_result = np.concatenate(
          [shard["x"] for shard in result_shards.collect()]
      )
  
      assert np.array_equal(result, expected_result)
  
imports: import os
  from unittest import TestCase
  import numpy
  import pytest
  import torch
  import torch.nn
  from zoo.orca import OrcaContext
  from zoo.orca.data.pandas import read_csv
  from zoo.orca.learn.metrics import Accuracy
  from zoo import init_nncontext
  from zoo.orca.learn.pytorch import Estimator
  from zoo.orca.data import SparkXShards
  from zoo.orca.data.image.utils import chunks
label_desc: Use image node_id 7 for calling the TestPyTorchEstimator obj's underlying member method code with example usage: obj.test_xshards_predict() without return types