iNdEx = postIndex
		default:
			iNdEx = preIndex
			skippy, err := skipGenerated(dAtA[iNdEx:])
			if err != nil {
				return err
			}
			if (skippy < 0) || (iNdEx+skippy) < 0 {
				return ErrInvalidLengthGenerated
			}
			if (iNdEx + skippy) > l {
				return io.ErrUnexpectedEOF
			}
			iNdEx += skippy
		}
	}

	if iNdEx > l {
		return io.ErrUnexpectedEOF
	}

            {{- end }}
            service.istio.io/canonical-name: {{ index .ObjectMeta.Labels `service.istio.io/canonical-name` | default (index .ObjectMeta.Labels `app.kubernetes.io/name`) | default (index .ObjectMeta.Labels `app`) | default .DeploymentMeta.Name  | quote }}

				buildPod().phase(v1.PodSucceeded).index("0").Pod,
				buildPod().phase(v1.PodFailed).index("1").Pod,
				buildPod().phase(v1.PodSucceeded).trackingFinalizer().index("1").Pod,
				buildPod().phase(v1.PodSucceeded).trackingFinalizer().index("3").Pod,
				buildPod().uid("a").phase(v1.PodFailed).trackingFinalizer().index("2").Pod,
				buildPod().uid("b").phase(v1.PodFailed).trackingFinalizer().index("5").Pod,
			},
			wantRmFinalizers: 4,

			c.checkindex(p, index1, 15)
			c.checkindex(p, index2, 15)
			imm5 |= 1
			imm5 |= index1 << 1
			imm4 |= index2
		case ARNG_H:
			c.checkindex(p, index1, 7)
			c.checkindex(p, index2, 7)
			imm5 |= 2
			imm5 |= index1 << 2
			imm4 |= index2 << 1
		case ARNG_S:
			c.checkindex(p, index1, 3)
			c.checkindex(p, index2, 3)
			imm5 |= 4
			imm5 |= index1 << 3
			imm4 |= index2 << 2

  if (ic->num_outputs() <= new_src.index) {
    status->status = tensorflow::errors::OutOfRange(
        "Cannot update edge. Output index [", new_src.index,
        "] is greater than the number of total outputs [", ic->num_outputs(),
        "].");
    return;
  }
  tensorflow::shape_inference::ShapeHandle shape = ic->output(new_src.index);

  tensorflow::shape_inference::InferenceContext* ic_dst =

  for (auto* n : ret_nodes) {
    int index;
    TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "index", &index));
    const int num_dtypes = send_from_host_dtypes.size();
    if (index < 0 || index >= num_dtypes) {
      return errors::Internal("Invalid _Retval index: ", index);
    }
    for (auto edge : n->in_edges()) {
      inputs[index] =

    auto* attr = node->attrs().Find("index");
    if (!attr)
      return errors::InvalidArgument(node->type_string(), " node '",
                                     node->name(),
                                     "' is missing attribute 'index'");

    auto index = attr->i();
    const int num_nodes = nodes->size();
    if (num_nodes < index + 1) nodes->resize(index + 1);

  BufferOffset<tflite::Buffer> empty_buffer_;

  std::vector<BufferOffset<tflite::Buffer>> buffers_;
  // Maps subgraph index and tensor name in the graph to the tensor index.
  absl::flat_hash_map<int, absl::flat_hash_map<std::string, int>>
      tensor_index_map_;

  // Maps op name to index of the corresponding OperatorCode in opcodes_ vector.
  absl::flat_hash_map<std::string, uint32_t> opcode_index_map_;

}

// Checks if the `index` bit of `val` is set.
template <class T>
constexpr bool IsSet(const T &val, unsigned index) {
  return (val & (1 << index)) != 0;
}

// Sets the `index` bit of `val`.
template <class T>
constexpr void Set(T &val, unsigned index) {
  val |= (1 << index);
}

// Unset the `index` bit of `val`.
template <class T>

        preWriteCleanup(now);

        int newCount = this.count - 1;
        AtomicReferenceArray<ReferenceEntry<K, V>> table = this.table;
        int index = hash & (table.length() - 1);
        ReferenceEntry<K, V> first = table.get(index);

        for (e = first; e != null; e = e.getNext()) {
          K entryKey = e.getKey();
          if (e.getHash() == hash
              && entryKey != null