code: def test_wrong_feature_shape():
      features = {
          "image/encoded": tfrec.FixedLenFeature((), tfrec.string, ""),
          "image/object/bbox": tfrec.FixedLenFeature(
              [], tfrec.float32, -1.0
          ),
          "image/object/class/label": tfrec.FixedLenFeature(
              [], tfrec.int64, -1
          ),
      }
      test_dummy_data_path = os.path.join(
          get_dali_extra_path(), "db", "coco_dummy"
      )
      pipe = Pipeline(1, 1, 0)
      with pipe:
          input = fn.readers.tfrecord(
              path=os.path.join(
                  test_dummy_data_path, "small_coco.tfrecord"
              ),
              index_path=os.path.join(
                  test_dummy_data_path, "small_coco_index.idx"
              ),
              features=features,
          )
      pipe.set_outputs(
          input["image/encoded"],
          input["image/object/class/label"],
          input["image/object/bbox"],
      )
      pipe.build()
      # the error is raised because FixedLenFeature is used with insufficient shape to house the input
      assert_raises(
          RuntimeError,
          pipe.run,
          glob="Error when executing CPU operator*readers*tfrecord*"
          "Output tensor shape is too small*[]*Expected at least 4 elements",
      )
  
imports: from nvidia.dali import Pipeline, pipeline_def
  import nvidia.dali.ops
  import nvidia.dali.fn
  import nvidia.dali.types
  import nvidia.dali.tfrecord
  import os.path
  import tempfile
  import numpy
  from test_utils import compare_pipelines, get_dali_extra_path
  from nose_utils import assert_raises
  from nose2.tools import cartesian_params
  from nose import SkipTest
label_desc: Use image node_id 6 for calling a global function with example usage: test_wrong_feature_shape() without return types