void TF_DeleteOutputList(TF_OutputList* o) { delete unwrap(o); }
void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {
  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}

  tensorflow::unwrap(ctx)->SetExecutorForThread(executor->executor());
}

TFE_Executor* TFE_ContextGetExecutorForThread(TFE_Context* ctx) {
  return new TFE_Executor(&tensorflow::unwrap(ctx)->Executor());
}

void TFE_HostAddressSpace(TFE_Context* ctx, TF_Buffer* buf) {
  auto address_space = tensorflow::DeviceNameUtils::AddressSpace(
      tensorflow::unwrap(ctx)->HostCPUParsedName());

  tensorflow::unwrap(ctx)->StartStep();
}

void TFE_ContextEndStep(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->EndStep();
}

const TFE_OpAttrs* TFE_OpGetAttrs(const TFE_Op* op) {
  return tensorflow::wrap(tensorflow::unwrap(op)->GetOpAttrs());
}

void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs) {
  tensorflow::unwrap(op)->AddAttrs(tensorflow::unwrap(attrs));
}

  public void testWrap() {
    assertThat(Primitives.wrap(int.class)).isSameInstanceAs(Integer.class);
    assertThat(Primitives.wrap(Integer.class)).isSameInstanceAs(Integer.class);
    assertThat(Primitives.wrap(String.class)).isSameInstanceAs(String.class);
  }

  public void testUnwrap() {
    assertThat(Primitives.unwrap(Integer.class)).isSameInstanceAs(int.class);
    assertThat(Primitives.unwrap(int.class)).isSameInstanceAs(int.class);

  public void testWrap() {
    assertThat(Primitives.wrap(int.class)).isSameInstanceAs(Integer.class);
    assertThat(Primitives.wrap(Integer.class)).isSameInstanceAs(Integer.class);
    assertThat(Primitives.wrap(String.class)).isSameInstanceAs(String.class);
  }

  public void testUnwrap() {
    assertThat(Primitives.unwrap(Integer.class)).isSameInstanceAs(int.class);
    assertThat(Primitives.unwrap(int.class)).isSameInstanceAs(int.class);

  int real_num_outputs = expected_max_outputs;
  TFE_OpSetCancellationManager(op_.get(), wrap(cancellation_manager_), status);
  if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

    return;
  }
  status->status = tensorflow::unwrap(ctx)->EnableCollectiveOps(server_def);
}

TF_CAPI_EXPORT extern void TFE_AbortCollectiveOps(TFE_Context* ctx,
                                                  TF_Status* status) {
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  auto collective_executor_handle = context->GetCollectiveExecutorHandle();

  TFE_TensorHandle* var_handle0 = TestVariable(ctx, 1.0, dev0_name);
  EXPECT_NE(var_handle0, nullptr);

  absl::Status status2;
  EXPECT_EQ(tensorflow::unwrap(var_handle0)->DeviceName(&status2), dev0_name);

  // Rename local device
  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();

      } else {
        TF_SetStatus(
            status, TF_INVALID_ARGUMENT,
            absl::StrCat(
                "Got a non-parallel tensor ",
                tensorflow::unwrap(absl::get<TFE_TensorHandle*>(input))
                    ->DebugString(),
                " as input to a parallel operation. First pack non-parallel "
                "tensors for each device into a parallel tensor explicitly.")

extern "C" {

TF_CAPI_EXPORT extern TFE_TensorDebugInfo* TFE_TensorHandleTensorDebugInfo(
    TFE_TensorHandle* h, TF_Status* status) {
  tensorflow::TensorHandle* handle =
      TensorHandleFromInterface(tensorflow::unwrap(h));
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }