code: def test_no_shuffling_inmem(
      synthetic_dataset, epochs, reader_factory
  ):
      with InMemBatchedDataLoader(
          reader_factory(
              synthetic_dataset.url,
              schema_fields=["^id$"],
              workers_count=1,
              shuffle_row_groups=False,
          ),
          num_epochs=epochs,
          rows_capacity=100,
      ) as loader:
          ids = [row["id"][0].numpy() for row in loader]
          # expected_ids would be [0, 1, 2, ...]
          # expected to be repeated epochs times
          expected_ids = [
              row["id"] for row in synthetic_dataset.data
          ] * epochs
          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 11 for calling a global function with example usage: test_no_shuffling_inmem(synthetic_dataset, epochs, reader_factory) without return types