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-rw-r--r--tensorflow/compiler/xla/client/client.h10
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/compiler/xla/client/client.h b/tensorflow/compiler/xla/client/client.h
index 7960b07868..6f4d33c469 100644
--- a/tensorflow/compiler/xla/client/client.h
+++ b/tensorflow/compiler/xla/client/client.h
@@ -96,8 +96,8 @@ class Client {
//
// If shape_with_layout is not nullptr, it points to a shape whose layout will
// be the layout of the returned literal.
- StatusOr<std::unique_ptr<Literal>> Transfer(
- const GlobalData& data, const Shape* shape_with_layout = nullptr);
+ StatusOr<Literal> Transfer(const GlobalData& data,
+ const Shape* shape_with_layout = nullptr);
// Transfer the given literal to the server. This allocates memory on the
// device and copies the literal's contents over. Returns a global data handle
@@ -122,7 +122,7 @@ class Client {
// device_handle and replica_id together specify a particular device; a device
// assigned for the given replica_id among the replicas that the given device
// handle belongs to.
- StatusOr<std::unique_ptr<Literal>> TransferFromOutfeed(
+ StatusOr<Literal> TransferFromOutfeed(
const Shape* shape_with_layout, int64 replica_id = 0,
const DeviceHandle* device_handle = nullptr);
@@ -132,7 +132,7 @@ class Client {
// Executes the computation with the given arguments and transfers the result
// to the client as a literal. Parameters are defined the same as for
// Execute() and Transfer().
- StatusOr<std::unique_ptr<Literal>> ExecuteAndTransfer(
+ StatusOr<Literal> ExecuteAndTransfer(
const XlaComputation& computation,
absl::Span<GlobalData* const> arguments,
const ExecutionOptions* execution_options = nullptr,
@@ -153,7 +153,7 @@ class Client {
//
// If output_layout is non-null, then the output of the computation will be
// stored using that layout.
- StatusOr<std::unique_ptr<Literal>> ComputeConstant(
+ StatusOr<Literal> ComputeConstant(
const XlaComputation& computation,
const Layout* output_layout = nullptr) const;