ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  auto* placeholder_t =
      TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build an abstract operation.
  auto* add_op = TF_NewAbstractOp(graph_ctx);
  TF_AbstractOpSetOpType(add_op, "Add", status.get());

   *
   * <p>The default behavior is to auto increment by zero. i.e: The ticker is left unchanged when
   * queried.
   */
  @SuppressWarnings("GoodTime") // should accept a java.time.Duration
  @CanIgnoreReturnValue
  public FakeTicker setAutoIncrementStep(long autoIncrementStep, TimeUnit timeUnit) {
    checkArgument(autoIncrementStep >= 0, "May not auto-increment by a negative amount");

0 0 auto;\n  width: auto;\n}\n\n.col-1 {\n  flex: 0 0 auto;\n  width: 8.33333333%;\n}\n\n.col-2 {\n  flex: 0 0 auto;\n  width: 16.66666667%;\n}\n\n.col-3 {\n  flex: 0 0 auto;\n  width: 25%;\n}\n\n.col-4 {\n  flex: 0 0 auto;\n  width: 33.33333333%;\n}\n\n.col-5 {\n  flex: 0 0 auto;\n  width: 41.66666667%;\n}\n\n.col-6 {\n  flex: 0 0 auto;\n  width: 50%;\n}\n\n.col-7 {\n  flex: 0 0 auto;\n  width: 58.33333333%;\n}\n\n.col-8 {\n  flex: 0 0 auto;\n  width: 66.66666667%;\n}\n\n.col-9 {\n  flex: 0 0 auto;\n...

   *
   * <p>The default behavior is to auto increment by zero. i.e: The ticker is left unchanged when
   * queried.
   */
  @SuppressWarnings("GoodTime") // should accept a java.time.Duration
  @CanIgnoreReturnValue
  public FakeTicker setAutoIncrementStep(long autoIncrementStep, TimeUnit timeUnit) {
    checkArgument(autoIncrementStep >= 0, "May not auto-increment by a negative amount");

# Auto detect text files and perform LF normalization
* text=auto

# Known text files
*.java text
*.xml text
*.yml text
*.md text
*.sh text
*.css text
*.txt text

# Known binary files

  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {
    unwrapped_outputs.push_back(handle);
  }
  for (int i = 0; i < *num_outputs; ++i) {
    auto logged_tensor = std::make_unique<LoggedTensor>(unwrapped_outputs[i]);
    outputs[i] = MakeLoggedTensorHandle(context, dev->device_name,

    /**
     * Create RDMA provider based on preference string
     *
     * @param preference provider preference ("auto", "disni", "tcp", etc.)
     * @return best available RDMA provider, or null if none available
     */
    public static RdmaProvider createProvider(String preference) {
        if ("auto".equalsIgnoreCase(preference)) {
            return selectBestProvider();
        }

        switch (preference.toLowerCase()) {

  if (dlMTensor->deleter != nullptr) {
    dlMTensor->deleter(dlMTensor);
  }
}

void* TFE_HandleToDLPack(TFE_TensorHandle* h, TF_Status* status) {
  auto tf_dlm_context = GetDlContext(h, status);
  if (!status->status.ok()) {
    return nullptr;
  }

  auto* tf_dlm_data = TFE_TensorHandleDevicePointer(h, status);
  if (!status->status.ok()) {
    return nullptr;
  }

 */
public class RdmaProviderFactoryTest {

    @Test
    public void testCreateProviderAuto() {
        RdmaProvider provider = RdmaProviderFactory.createProvider("auto");
        assertNotNull(provider, "Auto provider selection should return a provider");
        assertTrue(provider.isAvailable(), "Returned provider should be available");
    }

    @Test
    public void testCreateProviderTcp() {

                                    " into function '", fn_name, "'");

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;
      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {