}
				if retVal, err = util.MergeAnyWithAny(filter.GetTypedConfig(), userFilter.GetTypedConfig()); err != nil {
					retVal = filter.GetTypedConfig()
				}
			}
			filter.Name = filterName
			if retVal != nil {
				filter.ConfigType = &listener.Filter_TypedConfig{TypedConfig: retVal}
			}
		}
	}
	if filter.Name == wellknown.HTTPConnectionManager {

			}
			retVal = thisVal
		case 3: // match something like aaa[100]
			thisVal := val[match[1]]
			// If it is a list and looking for more down the path
			if thisVal.GetListValue() != nil && len(path) > 1 {
				index, _ := strconv.Atoi(match[2])
				return getConfigValue(path[1:], thisVal.GetListValue().Values[index].GetStructValue().Fields)
			}
			retVal = thisVal
		}
	}
	return retVal
}

      // CHECK-NEXT: }

      tf_executor.fetch %outputs_10#0 : tensor<?x2xi64>
    }
    func.return %0#0 : tensor<?x2xi64>
    // CHECK:      name: "ParseExample/ParseExampleV2"
    // CHECK-NEXT: op: "_Retval"
    // CHECK-NEXT: input: "ParseExample"

  }

                        if (subResult != null) {
                            retVal += subResult;
                        } else {
                            retVal += "$" + expr.substring(index + 1, lastIndex + 1);
                        }
                    }

                    retVal += evaluate(expr.substring(lastIndex + 1));
                    return retVal;
                }
            }

		},
	}
}

func createTestStatus(name string, status api.ConditionStatus, msg string, err string) *api.ComponentStatus {
	retVal := &api.ComponentStatus{
		Conditions: []api.ComponentCondition{
			{Type: api.ComponentHealthy, Status: status, Message: msg, Error: err},
		},
	}
	retVal.Name = name
	return retVal
}

func TestList_NoError(t *testing.T) {
	r := NewTestREST(testResponse{result: probe.Success, data: "ok"})

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  auto* retval_task0 =
      TFE_TensorHandleCopyToDevice(retvals[0], ctx, local_device_name, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
  auto b = ops::_Arg(scope.WithOpName("B"), DT_INT32, 1);
  auto c = ops::Add(scope.WithOpName("C"), a, b);
  auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
  TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
  return graph;
}

StatusOr<FunctionDef> SampleFuntionAddXY(const std::string& name) {
  TF_ASSIGN_OR_RETURN(auto graph, SampleGraphAddXY());

// following steps:
//
// 1. Add a XLA computation key placeholder node (it will be used as input for
//    XlaRecvAtHost and XlaSendFromHost);
// 2. Replace all _Arg nodes with one single XlaRecvAtHost node;
// 3. Replace all _Retval nodes with one single XlaSendFromHost node;
// 4. Mark all nodes except key placeholder with attr `xla_cluster_attr_name`
//    and `outside_compilation_attr_name`;

      count: 4
    }
  }
  unclustered_op_histogram {
    op: "NoOp"
    count: 2
  }
  unclustered_op_histogram {
    op: "_Arg"
    count: 1
  }
  unclustered_op_histogram {
    op: "_Retval"
    count: 1
  }
}
)",
      &expected_auto_clustering_activity);
  EXPECT_EQ(listener()->auto_clustering_activity().DebugString(),
            expected_auto_clustering_activity.DebugString());

               absl::Span<AbstractTensorHandle*> retvals, int* num_retvals,
               ForwardOperation* forward_op_, Tape* tape,
               const GradientRegistry& registry) {
  TF_RETURN_IF_ERROR(op_->Execute(retvals, num_retvals));
  for (int i = 0; i < *num_retvals; i++) {
    // TODO(srbs): Manage refcount of ForwardOperation's inputs/outputs.
    forward_op_->outputs.push_back(retvals[i]);
  }