code: def _tensor_arg_shape_kwarg():
      batch_sizes = [1, 2, 3, 16, 5]
      shape = [
          [
              np.array([1 + 3 * i, 2 * (i + 1) - 1], dtype=np.int32)
              for i in range(batch_size)
          ]
          for batch_size in batch_sizes
      ]
      mask_batches = [
          np.array([i % 2 for i in range(batch_size)], dtype=bool)
          for batch_size in batch_sizes
      ]
      return tuple(), mask_batches, {"shape": 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 37 for calling a global function with example usage: _tensor_arg_shape_kwarg() and returns: tuple,  mask_batches,  dict