retval := discovery.DiscoveryResponse{}
	if len(responses) == 0 {
		return &retval, nil
	}

	for _, response := range responses {
		// Combine all the shards as one, even if that means losing information about
		// the control plane version from each shard.
		retval.ControlPlane = response.ControlPlane
		retval.Resources = append(retval.Resources, response.Resources...)
	}

				}
				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
}

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

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

    for (auto input : inputs) {
      TF_RETURN_IF_ERROR(fn_op->AddInput(input));
    }
    int retvals = outputs.size() - null_indices.size();
    std::vector<AbstractTensorHandle*> fn_outputs(retvals);
    TF_RETURN_IF_ERROR(fn_op->Execute(
        absl::Span<AbstractTensorHandle*>(fn_outputs.data(), fn_outputs.size()),
        &retvals));
    int skipped_indices = 0;
    for (int i = 0; i < outputs.size(); i++) {

  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;
  TFE_Execute(func, &retvals[0], &num_retvals, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(1, num_retvals);
  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;

                              PatternRewriter &rewriter) {
  if (concat.getVal().size() < 2)
    return rewriter.notifyMatchFailure(
        concat, "Concatenate op should have at least two operands");

  auto first = concat.getVal()[0].getDefiningOp<mhlo::SliceOp>();
  auto second = concat.getVal()[1].getDefiningOp<mhlo::SliceOp>();
  if (!first || !second)

// CHECK-NEXT:    "tf.IfrtCall"(%arg0, [[ARRAYKEY]]) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [1 : i32]}> {__tpu_compile_metadata_text = "retvals { sharding { } }"} : (tensor<1x3xf32>, tensor<!tf_type.string>) -> tensor<1x1xf32>
// CHECK-NEXT:    return
//
 func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {

    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    TFE_TensorHandle* retvals[1];
    int num_retvals = 1;
    TFE_Execute(shape_op, &retvals[0], &num_retvals, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

    device_type = TFE_TensorHandleDeviceType(retvals[0], status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

	if err != nil {
		return nil, err
	}
	retval := make([]*v1alpha1.IstioOperator, 0)
	for _, un := range ul.Items {
		fixTimestampRelatedUnmarshalIssues(&un)
		by := util.ToYAML(un.Object)
		iop, err := operator_istio.UnmarshalIstioOperator(by, true)
		if err != nil {
			return nil, err
		}
		retval = append(retval, iop)
	}
	return retval, nil
}