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

  // The device of var_handle0 is local device which is the same before and
  // after cluster update. Remove resource with valid device should succeed.
  TFE_Op* op = TFE_NewOp(ctx, "DestroyResourceOp", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, var_handle0, status);
  TFE_OpSetDevice(op, dev0_name, status);

      tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding);
}

void TF_SetXlaConstantFoldingDisabled(unsigned char should_enable) {
  tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding =
      static_cast<bool>(should_enable);
}

void TF_SetXlaMinClusterSize(int size) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();

  }

  ASSERT_NO_FATAL_FAILURE(CompareNumericalAndAutodiffGradients(
      DivNoNanModel, DivNoNanGradModel, immediate_execution_ctx_.get(),
      {x.get(), y.get()}, UseFunction()));

  // `DivNoNanGradModel` should return {`0`, `0`} when the denominator is `0`.
  AbstractTensorHandlePtr z;
  {
    AbstractTensorHandle* z_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(

}

static void Sync(const TF_WritableFile* file, TF_Status* status) {
  // For historical reasons, this does the same as `Flush` at the moment.
  // TODO(b/144055243): This should use `fsync`/`sync`.
  Flush(file, status);
}

static void Close(const TF_WritableFile* file, TF_Status* status) {
  auto posix_file = static_cast<PosixFile*>(file->plugin_file);

  TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status);
  TFE_OpSetAttrString(op.get(), "container", "", 0);
  // Use the special GUID for no buffer sharing
  //
  // TODO(allenl): Should we provide a better API for this? AFAIK this is the
  // only reasonable way to make variables with no aliasing using the eager C
  // API.
  std::string no_sharing = "cd2c89b7-88b7-44c8-ad83-06c2a9158347";

    const TFE_OpAttrs* attributes, int expected_max_outputs,
    TF_Status* status) {
  absl::optional<std::vector<MaybeParallelTensorOwned>> result;
  // TODO(allenl): We should remove "TPU" from these op names at the very least,
  // or consider other ways of packing/unpacking parallel tensors.
  if (operation_name == std::string("TPUReplicatedInput")) {

//
// DeviceThread itself is thread-safe, in that StartExecute will block if there
// is a pending execution. Since StartExecute is equivalent to grabbing a lock,
// multiple DeviceThreads should always be accessed in the same order to avoid
// deadlocks.
class DeviceThread {
 public:
  // Starts a background thread waiting for `StartExecute`.
  explicit DeviceThread(const std::string& device, const bool is_async,

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

  // Un-pack the parallel tensor to verify that the operation was
  // successful. The resulting structure should be:
  //   second_device{first_device{1. * 3., 2. * 3.}, 3. * 3.}.
  std::array<TensorHandlePtr, 2> second_components;
  ExtractPerDeviceValues(context.get(), multiply_result.get(),

#endif  // !IS_MOBILE_PLATFORM
  return tensorflow::wrap(eager_context);
}

void TFE_DeleteContext(TFE_Context* ctx) {
  if (ctx == nullptr) {
    return;
  }

  // ctx->RefCountIsOne() should be true here.
  tensorflow::unwrap(ctx)->Release();
}

TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) {
  TF_DeviceList* l = new TF_DeviceList;

        tensorflow::unwrap(op_to_reset);
    op->Clear();
    status->status = op->Reset(op_or_function_name, raw_device_name);
  } else {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "op_to_reset should not be nullptr");
  }
}

void TFE_ContextEnableGraphCollection(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(true);
}

void TFE_ContextDisableGraphCollection(TFE_Context* ctx) {