code: def test_no_shuffling(
      synthetic_dataset, reader_factory, data_loader_type
  ):
      with data_loader_type(
          reader_factory(
              synthetic_dataset.url,
              schema_fields=["^id$"],
              workers_count=1,
              shuffle_row_groups=False,
          )
      ) as loader:
          ids = [row["id"][0].numpy() for row in loader]
          # expected_ids would be [0, 1, 2, ...]
          expected_ids = [row["id"] for row in synthetic_dataset.data]
          np.testing.assert_array_equal(expected_ids, ids)
  
imports: from decimal import Decimal
  from packaging import version
  import numpy
  import pyarrow
  import pytest
  import torch
  from petastorm import make_reader, TransformSpec, make_batch_reader
  from petastorm.pytorch import _sanitize_pytorch_types, DataLoader, BatchedDataLoader, decimal_friendly_collate, InMemBatchedDataLoader, _load_rows_into_mem
  from petastorm.tests.test_common import TestSchema
label_desc: Use image node_id 10 for calling a global function with example usage: test_no_shuffling(synthetic_dataset, reader_factory, data_loader_type) without return types