code: def _run_test(
      device,
      batch_size,
      dim,
      axes,
      axis_names,
      batch_norm,
      out_type=None,
      in_type=None,
      shift=None,
      scale=None,
  ):
      kind = "inter-sample" if batch_norm else "per-sample"
      msg = "{0}, {1}, batch = {2}, dim = {3}".format(
          device, kind, batch_size, dim
      )
      if axes is not None:
          msg += " axes = {}".format(axes)
      if axis_names is not None:
          msg += " axis_names = '{}'".format(axis_names)
      if out_type is not None:
          msg += " output = {}".format(out_type)
      if in_type is not None:
          msg += " input = {}".format(in_type)
      print(msg)
  
      pipe = NormalizePipeline(
          device,
          batch_size,
          dim,
          axes,
          axis_names,
          batch_norm,
          out_type,
          in_type,
          shift,
          scale,
      )
      pipe.build()
      for iter in range(2):
          out = pipe.run()
          pipe.check_batch(*[to_list(x) for x in out])
  
imports: from nvidia.dali.pipeline import Pipeline
  from nvidia.dali import backend
  import nvidia.dali.ops
  import numpy
  from test_utils import dali_type
  from nvidia.dali import fn, pipeline_def, types
  from nose2.tools import params
label_desc: Use image node_id 17 for calling a global function with example usage: _run_test(device, batch_size, dim, axes, axis_names, batch_norm, out_type, in_type, shift, scale) without return types