std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

namespace {

struct LoggingDevice {
  tensorflow::string device_name;
  tensorflow::string underlying_device;
  // Set to true whenever a TensorHandle is copied onto the device
  bool* arrived_flag;
  // Set to true whenever an operation is executed
  bool* executed_flag;
  // If true, only explicit op placements are accepted. If false, uses
  // type-based dispatch.
  bool strict_scope_placement;
};

}
TEST(CAPI, RemoteExecuteSilentCopiesAsync) {
  TestRemoteExecuteSilentCopiesOp(/*async=*/true, /*remote=*/true);
}
TEST(CAPI, RemoteExecuteSilentCopiesLocal) {
  TestRemoteExecuteSilentCopiesOp(/*async=*/false, /*remote=*/false);
}
TEST(CAPI, RemoteExecuteSilentCopiesLocalAsync) {
  TestRemoteExecuteSilentCopiesOp(/*async=*/true, /*remote=*/false);
}

  auto allocator = std::make_unique<AsyncValueAllocator>();
  tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto pjrt_client,
      xla::GetTfrtCpuClient(/*asynchronous=*/true, /*cpu_device_count=*/1));
  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto buffer,
      pjrt_client->BufferFromHostBuffer(

                       /*executing_eagerly*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*executing_eagerly*/ ::testing::Values(true, false)));
#endif
}  // namespace
}  // namespace internal

namespace tensorflow {
namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),
      func_record_(new FunctionRecord(std::move(fdef), {}, true)) {}
GraphFunction::~GraphFunction() {}
Status GraphFunction::GetFunctionDef(const FunctionDef **fdef) {
  *fdef = &(func_record_->fdef());
  return absl::OkStatus();
}
}  // namespace graph
}  // namespace tracing

    CustomGradientTest, CustomGradientTest,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*executing_eagerly*/ ::testing::Values(true, false)));

}  // namespace
}  // namespace internal
}  // namespace gradients

  cache2.Insert("a", 1);
  cache2.Insert("b", 2);
  cache2.Insert("c", 3);
  cache2.Insert("d", 4);
  EXPECT_TRUE(cache2.Lookup("a", &value));
  EXPECT_EQ(value, 1);
  EXPECT_TRUE(cache2.Lookup("b", &value));
  EXPECT_EQ(value, 2);
  EXPECT_TRUE(cache2.Lookup("c", &value));
  EXPECT_EQ(value, 3);
  EXPECT_TRUE(cache2.Lookup("d", &value));
  EXPECT_EQ(value, 4);

  // ready.
  TestRemoteExecuteSilentCopiesFunc(/*async=*/true, /*remote=*/true,
                                    /*heavy_load_on_streaming_rpc=*/true,
                                    /*remote_func_outputs*/ true,
                                    /*has_packed_input=*/true);
}

                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_.attrs.Set(attr_name, proto);