code: def _tensor_arg_transform_per_dim_params(arg_name):
      def inner():
          batch_sizes = [5, 1, 2, 8]
          mask_batches = [
              np.array([i % 2 for i in range(batch_size)], dtype=bool)
              for batch_size in batch_sizes
          ]
          kwarg_batches = [
              np.array(
                  [[pred, pred] for pred in mask], dtype=np.float32
              )
              for mask in mask_batches
          ]
          return tuple(), mask_batches, {arg_name: kwarg_batches}
  
      return inner
  
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 34 for calling a global function with example usage: _tensor_arg_transform_per_dim_params(arg_name) and returns: inner, tuple,  mask_batches,  dict