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 {

    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> {

	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
}

		if dst != nil && srcPatch != nil {
			retVal, errMerge := util.MergeAnyWithAny(dst, srcPatch)
			if errMerge != nil {
				return false, fmt.Errorf("function MergeAnyWithAny failed for ApplyClusterMerge: %v", errMerge)
			}

			// Merge the above result with the whole cluster
			merge.Merge(dst, retVal)
		}
	}
	return true, nil
}

  kAsync,
};

enum class DeviceCompileState {
  kUncompiled,
  kCompiling,
  kCompiled,
};

// Creates a single-node graph using the specified `node_def` as the only op
// apart from the arg and retval nodes corresponding to `args` and
// `result_types` respectively.
absl::StatusOr<std::unique_ptr<Graph>> CreateSingleOpGraph(
    const NodeDef& node_def, absl::Span<const XlaArgument> args,

Status TapeOperation::Execute(absl::Span<AbstractTensorHandle*> retvals,
                              int* num_retvals) {
  TF_RETURN_IF_ERROR(parent_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]);
  }