void* device_info);

  // Method to execute an operation.
  //
  // Arguments provide enough information to reconstruct the original `TFE_Op`,
  // or construct a transformed version, by inspecting the passed `op`.
  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may

*   `tf.SavedModel`

    *   Introduced class method `tf.saved_model.experimental.Fingerprint.from_proto(proto)`, which can be used to construct a `Fingerprint` object directly from a protobuf.
    *   Introduced member method `tf.saved_model.experimental.Fingerprint.singleprint()`, which provides a convenient way to uniquely identify a SavedModel.

// Sets a thread-local device placement policy. After this call, other calls to
// TFE_Execute in the same thread will use the device policy specified here
// instead of the device policy used to construct the context. This has no
// effect on the device policy used by other program threads.
TF_CAPI_EXPORT extern void TFE_ContextSetThreadLocalDevicePlacementPolicy(
    TFE_Context* ctx, TFE_ContextDevicePlacementPolicy policy);

  if (gpu_device.empty()) return;

  RunMinTest(gpu_device, /*use_XLA=*/true);
}

TEST(CAPI, SessionPRun) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Construct the graph: A + 2 + B
  TF_Operation* a = Placeholder(graph, s, "A");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Operation* b = Placeholder(graph, s, "B");

// parsed.
//
// Furthermore, as `INSTANTIATE_TEST_SUITE_P` needs to be at global level and we
// don't want to have a `std::vector<std::string>` at global level, we use a
// static pointer to such a vector: we construct it via `SchemeVector()` below
// and when tests are instantiated we process it using `GetSchemes()`.
static std::vector<std::string>* SchemeVector() {
  static std::vector<std::string>* schemes = new std::vector<std::string>;

                                                       TFE_TensorHandle* tensor,
                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,

namespace tensorflow {
namespace parallel_device {

// A helper for performing common operations on variables. A much more
// restricted stand-in for tf.Variable in Python.
class Variable {
 public:
  // Construct a Variable from a resource-dtype TFE_TensorHandle and an
  // indication of the dtype of the variable's value.
  //
  // Note that creating this resource-dtype handle can fail, so `Create` is a

TF_CAPI_EXPORT extern void TF_FunctionToFunctionDef(TF_Function* func,
                                                    TF_Buffer* output_func_def,
                                                    TF_Status* status);

// Construct and return the function whose FunctionDef representation is
// serialized in `proto`. `proto_len` must equal the number of bytes
// pointed to by `proto`.
// Returns: