code: def test_data_container_batching(
      batch_size,
      include_incomplete_pass,
      default_value,
      expected_data_containers,
  ):
      data_container = DACT(
          ids=[str(i) for i in range(10)],
          data_inputs=np.array(list(range(10))),
          expected_outputs=np.array(list(range(10, 20))),
      )
  
      # When
      data_containers = []
      for dc in data_container.minibatches(
          batch_size=batch_size,
          keep_incomplete_batch=include_incomplete_pass,
          default_value_data_inputs=default_value,
      ):
          data_containers.append(dc)
  
      # Then
      assert len(expected_data_containers) == len(data_containers)
      for expected_data_container, actual_data_container in zip(
          expected_data_containers, data_containers
      ):
          np.array_equal(
              expected_data_container.ids, actual_data_container.ids
          )
          np.array_equal(
              expected_data_container.data_inputs,
              actual_data_container.data_inputs,
          )
          np.array_equal(
              expected_data_container.expected_outputs,
              actual_data_container.expected_outputs,
          )
  
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 2 for calling a global function with example usage: test_data_container_batching(batch_size, include_incomplete_pass, default_value, expected_data_containers) without return types