code: def numpy_reader_roi_pipe(
      file_root,
      device="cpu",
      file_filter="*.npy",
      roi_start=None,
      rel_roi_start=None,
      roi_end=None,
      rel_roi_end=None,
      roi_shape=None,
      rel_roi_shape=None,
      roi_axes=None,
      default_axes=[],
      out_of_bounds_policy=None,
      fill_value=None,
  ):
      data = fn.readers.numpy(
          device=device,
          file_root=file_root,
          file_filter=file_filter,
          shard_id=0,
          num_shards=1,
          cache_header_information=False,
      )
      roi_data = fn.readers.numpy(
          device=device,
          file_root=file_root,
          file_filter=file_filter,
          roi_start=roi_start,
          rel_roi_start=rel_roi_start,
          roi_end=roi_end,
          rel_roi_end=rel_roi_end,
          roi_shape=roi_shape,
          rel_roi_shape=rel_roi_shape,
          roi_axes=roi_axes,
          out_of_bounds_policy=out_of_bounds_policy,
          fill_value=fill_value,
          shard_id=0,
          num_shards=1,
          cache_header_information=False,
      )
      sliced_data = fn.slice(
          data,
          start=roi_start,
          rel_start=rel_roi_start,
          end=roi_end,
          rel_end=rel_roi_end,
          shape=roi_shape,
          rel_shape=rel_roi_shape,
          axes=roi_axes
          or default_axes,  # Slice has different default (axis_names="WH")
          out_of_bounds_policy=out_of_bounds_policy,
          fill_values=fill_value,
      )
      return roi_data, sliced_data
  
imports: from nvidia.dali import Pipeline, pipeline_def
  import nvidia.dali.fn
  import nvidia.dali.types
  import nvidia.dali.backend
  import numpy
  from numpy.testing import assert_array_equal
  import os
  import platform
  import random
  import tempfile
  from nose_utils import assert_raises
  from nose import SkipTest
  from nose2.tools import params, cartesian_params
  from test_utils import compare_pipelines, to_array
label_desc: Use image node_id 20 for calling a global function with example usage: numpy_reader_roi_pipe(file_root, device, file_filter, roi_start, rel_roi_start, roi_end, rel_roi_end, roi_shape, rel_roi_shape, roi_axes, default_axes, out_of_bounds_policy, fill_value) and returns: roi_data,  sliced_data