vals.resize(size);
  std::iota(vals.begin(), vals.end(), start);

  TensorType ty =
      tensorflow::GetTypeFromTFTensorShape({size}, builder->getIntegerType(64));
  return DenseIntElementsAttr::get(ty, vals);
}

// Returns a 1-d i64 elements attribute populated with `val` repeated `size`
// times.
static DenseIntElementsAttr GetI64ElementsAttrForValue(int size, int64_t val,

	}

	modesStr := helper.GetAccessModesAsString(obj.Spec.AccessModes)
	reclaimPolicyStr := string(obj.Spec.PersistentVolumeReclaimPolicy)

	aQty := obj.Spec.Capacity[api.ResourceStorage]
	aSize := aQty.String()

	phase := obj.Status.Phase
	if obj.ObjectMeta.DeletionTimestamp != nil {
		phase = "Terminating"
	}
	volumeMode := "<unset>"
	if obj.Spec.VolumeMode != nil {

With inconsistent num_segments, the op still runs. only difference is,
the output takes the size of num_segments irrespective of size of segment_ids and data.
for num_segments less than expected output size, the last elements are ignored
for num_segments more than the expected output size, last elements are assigned 
smallest possible value for the specific numeric type.

For example: