code: def test_data_container_minibatch_should_be_lazy_and_use_getitem_when_data_is_lazy_loadable():
      items = [LoadableItem() for _ in range(10)]
      data_inputs = SomeLazyLoadableCollection(items)
      expected_outputs = SomeLazyLoadableCollection(
          [LoadableItem() for _ in range(10)]
      )
      data_container = DACT(
          data_inputs=data_inputs, expected_outputs=expected_outputs
      )
  
      i = 0
      batch_size = 2
      for batch in data_container.minibatches(batch_size=batch_size):
          assert len(batch) == batch_size
          assert all(
              item.is_loaded()
              for item in data_inputs.inner_list[: (i * batch_size)]
          )
          for y in range((i + 1) * batch_size, len(data_inputs)):
              assert not items[y].is_loaded()
          i += 1
  
file_path: Neuraxle/testing_neuraxle/test_data_container_batching.py
imports: import pytest
  from neuraxle.data_container import DACT, StripAbsentValues
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_data_container_minibatch_should_be_lazy_and_use_getitem_when_data_is_lazy_loadable() without return types