"and will trigger an error in the future. Either don't modify "
          "nodes after running them or create a new session.");
    }
  }
}

namespace {

// Helper method that creates a shape handle for a shape described by dims.
tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims(
    tensorflow::shape_inference::InferenceContext* ic, int num_dims,

  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(
                  server_def, tensorflow::Env::Default(), &worker_server2)

using tensorflow::tracing::TracingTensorHandle;

void TF_SetTracingImplementation(const char* name, TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s, SetDefaultTracingEngine(name));
}

// Creates a new TensorFlow function, it is an execution context attached to a
// given tracing context.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* s) {
  return wrap(CreateTracingExecutionContext(fn_name, s));

  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,

  void MarkAsResult(AbstractTensorHandle* gradient) const override;

  void DeleteGradient(AbstractTensorHandle* gradient) const override;

 private:
  // The context where the aggregation op `Add` is to be created.
  AbstractContext* ctx_;
};

// Returns the number of elements in the gradient tensor.
int64_t TapeVSpace::NumElements(AbstractTensorHandle* tensor) const {

  tensorflow::Status s = tensorflow::GrpcServer::Create(
      updated_server_def, tensorflow::Env::Default(), &worker_server);
  ASSERT_TRUE(s.ok()) << s.message();
  ASSERT_TRUE(worker_server->Start().ok());

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Create a new tensor_handle.

      tf_posix_filesystem::NewAppendableFile;
  ops->filesystem_ops->new_read_only_memory_region_from_file =
      tf_posix_filesystem::NewReadOnlyMemoryRegionFromFile;
  ops->filesystem_ops->create_dir = tf_posix_filesystem::CreateDir;
  ops->filesystem_ops->delete_file = tf_posix_filesystem::DeleteFile;
  ops->filesystem_ops->delete_dir = tf_posix_filesystem::DeleteDir;
  ops->filesystem_ops->rename_file = tf_posix_filesystem::RenameFile;

    TF_DeleteStatus(status);
  }

  // 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");

//    only worker 1.
// 2. Create a context B using A.
// 3. Create the variable in B.
// 4. Create another single host server def C with only worker 0.
// 5. Start the GRPC server for worker 0 using C.
// 6. Create a context D using the full cluster server def E.
// 7. Read the variable in D.
TEST(CAPI, SingleHostServerDefV1Works) {
  // Create a server def that represents a 2-process cluster and a client.

//     probe for gadgets inside core TensorFlow. We can even protect the area
//     of memory where the copies reside to not allow any more writes to it
//     after all copies are created.
template <typename T>
static std::unique_ptr<const T> CopyToCore(const T* plugin_ops,
                                           size_t plugin_size) {
  if (plugin_ops == nullptr) return nullptr;