code: def check_rel_start_rel_shape(
      device,
      batch_size,
      num_threads,
      get_dynamic_axes=None,
      args_device="cpu",
  ):
      image_gen = generator_random_data(
          batch_size,
          min_sh=(10, 10, 3),
          max_sh=(100, 100, 3),
          dtype=np.float32,
          val_range=[0.0, 1.0],
      )
  
      # Args GPU only possible with GPU backend
      assert args_device == device or device == "gpu"
  
      @pipeline_def(
          batch_size=batch_size, num_threads=num_threads, device_id=0
      )
      def make_pipe():
          image = fn.external_source(source=image_gen)
          if device == "gpu":
              image = image.gpu()
          if get_dynamic_axes:
              axes, rel_start, rel_shape = fn.external_source(
                  source=get_dynamic_axes, num_outputs=3
              )
          else:
              axes = types.Constant(
                  np.array([0, 1], dtype=np.int32), device="cpu"
              )
              rel_start = fn.random.uniform(
                  range=(0.1, 0.2),
                  shape=(2,),
                  dtype=types.FLOAT,
                  device=args_device,
              )
              rel_shape = fn.random.uniform(
                  range=(0.4, 0.6),
                  shape=(2,),
                  dtype=types.FLOAT,
                  device=args_device,
              )
          if args_device == "gpu":
              sliced = fn.slice(image, rel_start, rel_shape, axes=axes)
              return image, axes, rel_start, rel_shape, sliced
          else:
              sliced1 = fn.slice(
                  image,
                  rel_start=rel_start,
                  rel_shape=rel_shape,
                  axes=axes,
              )
              sliced2 = fn.slice(image, rel_start, rel_shape, axes=axes)
              return image, axes, rel_start, rel_shape, sliced1, sliced2
  
      pipe = make_pipe()
      pipe.build()
      ndim = 3
      for _ in range(3):
          outs = pipe.run()
          for sample_idx in range(batch_size):
              in_img = as_array(outs[0][sample_idx])
              axes = as_array(outs[1][sample_idx])
              naxes = axes.shape[0]
              if naxes == 0:  # Empty axes mean "all axes"
                  axes = np.array(range(ndim), dtype=np.int32)
              rel_start = as_array(outs[2][sample_idx])
              rel_shape = as_array(outs[3][sample_idx])
              start = np.zeros([ndim], dtype=np.int32)
              end = np.array(
                  [in_img.shape[i] for i in range(ndim)], dtype=np.int32
              )
              for i in range(len(axes)):
                  a = axes[i]
                  assert a >= -ndim and a <= (ndim - 1)
                  start[a] = roundint(rel_start[i] * in_img.shape[a])
                  end[a] = roundint(
                      (rel_start[i] + rel_shape[i]) * in_img.shape[a]
                  )
              ref_sliced = in_img[
                  start[0] : end[0],
                  start[1] : end[1],
                  start[2] : end[2],
              ]
              # With GPU arguments we don't test named arguments
              for out_idx in range(2 if args_device == "cpu" else 1):
                  sliced = as_array(outs[4 + out_idx][sample_idx])
                  np.testing.assert_allclose(ref_sliced, sliced)
  
imports: from nvidia.dali import Pipeline, pipeline_def, ops, fn, types
  import numpy
  import os
  from functools import partial
  from math import floor
  from test_utils import compare_pipelines, get_dali_extra_path, RandomDataIterator, generator_random_axes_for_3d_input, generator_random_data, as_array
  from nose_utils import assert_raises
  from nose2.tools import params
label_desc: Use image node_id 23 for calling a global function with example usage: check_rel_start_rel_shape(device, batch_size, num_threads, get_dynamic_axes, args_device) and returns: image,  axes,  rel_start,  rel_shape,  sliced, image,  axes,  rel_start,  rel_shape,  sliced1,  sliced2