code: def __init__(
      self,
      num_workers: int,
      expect_ds: bool,
      expect_sizes: Optional[dict],
      **kwargs,
  ):
      def train_loop_per_worker():
          data_shard = train.get_dataset_shard("train")
          assert isinstance(data_shard, DataIterator), data_shard
          for k, v in expect_sizes.items():
              shard = train.get_dataset_shard(k)
              if v == -1:
                  assert shard is None, shard
              else:
                  count = 0
                  for batch in shard.iter_batches():
                      for arr in batch.values():
                          count += arr.size
                  assert count == v, shard
  
      kwargs.pop("scaling_config", None)
      super().__init__(
          train_loop_per_worker=train_loop_per_worker,
          scaling_config=ScalingConfig(num_workers=num_workers),
          **kwargs,
      )
  
imports: from typing import Optional
  import random
  import pytest
  from unittest.mock import MagicMock
  import ray
  from ray import train
  from ray.train import DataConfig, ScalingConfig
  from ray.data import DataIterator
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.data._internal.execution.interfaces.execution_options import ExecutionOptions
  from ray.tests.conftest import *
label_desc: Use image node_id 1 to create a new TestBasic object from inherited base classes: DataParallelTrainer with example: obj = TestBasic(num_workers, expect_ds, expect_sizes)