//

  // Create `worker_1` using single host server def `worker_1_server_def`.
  tensorflow::ServerDef worker_1_server_def =
      CreateSingleHostServerDef(cluster_server_def, 1);
  worker_1_server_def.set_task_index(1);
  worker_1_server_def.set_job_name("worker");

  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(worker_1_server_def,

  }

  // 6. Read `var` using `ctx_1`. This read should succeed since `ctx_1` was
  // created with `isolate_session_state` set to false, and update should
  // preserve it.
  {
    // Create a handle to `var`, using `ctx_1`.
    TFE_TensorHandle* var_handle =
        CreateVarHandle(ctx_1, remote_device, /*variable_name=*/"var");

    TFE_TensorHandle* handle_1 = nullptr;
    int num_retvals = 1;

}

TEST_F(CApiAttributesTest, ShapeList) {
  const int64_t shape_1[] = {1, 3};
  const int64_t shape_2[] = {2, 4, 6};
  const int64_t* list[] = {&shape_1[0], &shape_2[0]};
  const size_t list_size = TF_ARRAYSIZE(list);
  const int ndims[] = {TF_ARRAYSIZE(shape_1), TF_ARRAYSIZE(shape_2)};
  const int total_ndims = 5;  // ndims[0] + ndims[1]

  auto desc = init("list(shape)");

    // TODO(b/168850692): Fix this.
    return errors::Unimplemented(
        "BroadcastMul is not supported in tracing mode yet.");
  }
  auto imm_ctx = dyn_cast<ImmediateExecutionContext>(ctx);
  AbstractTensorPtr minus_1(imm_ctx->CreateInt32Scalar(-1));
  ImmediateTensorHandlePtr dim(imm_ctx->CreateLocalHandle(minus_1.get()));
  AbstractTensorHandle* expand_dims_outputs;
  TF_RETURN_IF_ERROR(

  Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {5, 5});
  VerifyFDef({"add_1"}, M({{"feed1"}, {"feed2"}}), M({{"add"}, {"add_0"}}),
             {{"feed1", "add_1:0"},
              {"feed2", "add_1:1"},
              {"add_1:sum:0", "add"},
              {"add_1:sum:0", "add_0"}},
             {});
}

TEST_F(CApiFunctionTest, TwoDuplicateOutputs_OutputNames) {
  /*

// --------------------------------------------------------------------------

#ifdef __cplusplus
extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by

-				       __uint32_t, size_t);
+#if defined _FORCE_INLINES
+# if _STRING_ARCH_unaligned
 __STRING_INLINE void *
 __mempcpy_small (void *__dest1,
 		 char __src0_1, char __src2_1, char __src4_1, char __src6_1,
@@ -298,44 +139,7 @@ __mempcpy_small (void *__dest1,
     }
   return (void *) __u;
 }
-#   else
-#    ifndef _FORCE_INLINES
-#     define __mempcpy_args(src) \

  auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.

// called after a successful TF_NewWhile() call.
TF_CAPI_EXPORT extern void TF_AbortWhile(const TF_WhileParams* params);

// Adds operations to compute the partial derivatives of sum of `y`s w.r.t `x`s,
// i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
//
// `dx` are used as initial gradients (which represent the symbolic partial
// derivatives of some loss function `L` w.r.t. `y`).
// `dx` must be nullptr or have size `ny`.