code: def _tensor_arg_roi_random_crop_params():
      batch_sizes = [1, 2, 7, 3]
      crop_shape = [
          [
              np.array([100 * i + 50, 200 * i + 50, 3], dtype=np.int32)
              for i in range(batch_size)
          ]
          for batch_size in batch_sizes
      ]
      roi_start = [
          [
              np.array(
                  [sample[0] // 2, sample[1] // 2, sample[2]],
                  dtype=np.int32,
              )
              for sample in batch
          ]
          for batch in crop_shape
      ]
      mask_batches = [
          np.array([i % 2 for i in range(batch_size)], dtype=bool)
          for batch_size in batch_sizes
      ]
      return (
          tuple(),
          mask_batches,
          {
              "crop_shape": crop_shape,
              "roi_start": roi_start,
              "roi_end": crop_shape,
          },
      )
  
imports: from nvidia.dali.pipeline import pipeline_def, experimental
  import nvidia.dali.fn
  import nvidia.dali.types
  from nvidia.dali.types import SampleInfo
  from nvidia.dali import _conditionals
  from nvidia.dali.data_node import DataNode
  import numpy
  import os
  from test_utils import check_batch, compare_pipelines
  from nose_utils import assert_raises
  from test_utils import get_dali_extra_path
  from nose2.tools import params
  import itertools
label_desc: Use image node_id 36 for calling a global function with example usage: _tensor_arg_roi_random_crop_params() and returns: tuple,  mask_batches,  dict