// checkpoint when fetched.
    saver_def.set_save_tensor_name(absl::StrCat(save_node_name, ":0"));
    return saver_def;
  } else {
    return absl::InternalError(
        absl::StrCat("Failed to create SaverDef. Fields should be either all "
                     "empty strings or all non-empty strings. Got fields: ",
                     absl::StrJoin(fields, ",")));
  }
}

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

static void DeleteArray(void* data, size_t size, void* arg) {
  DCHECK_EQ(data, arg);
  delete[] reinterpret_cast<char*>(arg);
}

// Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
// result in a zero-sized tensor.
static TF_Tensor* EmptyTensor(TF_DataType dtype,
                              const tensorflow::TensorShape& shape) {

                 std::vector<Output>* grad_outputs) {
  // Propagate the incoming gradient along all the selected values,
  // and zero everywhere else. Use the Pad operator for this.
  //
  // First create an Nx2 padding where N is the number of input
  // dimensions. The first column is the number of prepended zeros
  // for each dimension, and the second column is the number of
  // appended zeros.
  //

  EXPECT_EQ(compilation_status.Delta(kOldBridgeWithFallbackModeSuccess), 0);
}

TEST(LegalizeTFTest, RecordsStreamzForNoMlirFallback) {
  FunctionDef my_func =
      tensorflow::FunctionDefHelper::Create("empty", {}, {}, {}, {}, {});

  tensorflow::FunctionDefLibrary fdef;
  *(fdef.add_function()) = my_func;
  tensorflow::FunctionLibraryDefinition flib_def(
      tensorflow::OpRegistry::Global(), fdef);

  TF_CHECK_OK(env->NewWritableFile(dot_h_fname, &h));
  TF_CHECK_OK(env->NewWritableFile(dot_cc_fname, &cc));
  string op_header_guard;
  StartFiles(false, dot_h_fname, h.get(), cc.get(), &op_header_guard);

  // Create the internal versions of these files for the hidden ops.
  std::unique_ptr<WritableFile> internal_h = nullptr;
  std::unique_ptr<WritableFile> internal_cc = nullptr;
  const string internal_dot_h_fname = MakeInternal(dot_h_fname);

  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
            std::initializer_list<TF_Output> outputs,
            const std::vector<std::vector<int32_t>>& expected_results) {
    // Create a session for this graph
    CSession csession(host_graph_, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    // Run
    csession.SetInputs(inputs);
    csession.SetOutputs(outputs);

  island_op.getControl().dropAllUses();

  // Break up all islands by simply creating a new island wrapping each
  // individual sub op. Do not create any control dependencies between the
  // newly created islands.
  SplitIsland(island_op, tf_executor::ControlType::get(&getContext()));

  // None of the originally given control deps are necessary.
  tf_executor::FetchOp fetch_op = graph_op.GetFetch();

  }
  result.emplace(std::move(result_content));
  return result;
}

// Used as an argument to TFE_NewCustomDeviceTensorHandle, indicating how
// ParallelTensors wrapped in TFE_TensorHandles should be cleaned up once their
// reference counts drop to zero.
void ParallelTensorDeallocator(void* data) {
  delete reinterpret_cast<ParallelTensor*>(data);
}

class KernelBuilderFactory
    : public ::tensorflow::kernel_factory::OpKernelFactory {
 public:
  explicit KernelBuilderFactory(TF_KernelBuilder* builder)
      : builder_(builder) {}
  ::tensorflow::OpKernel* Create(
      ::tensorflow::OpKernelConstruction* context) override {
    if (builder_->compute_function)
      return new ::tensorflow::COpKernel(context, builder_->create_function,

          attr_cst =
              builder.create<ConstOp>(op->getLoc(), output_type, attribute);
        } else {
          attr_cst = builder.create<mlir::arith::ConstantOp>(
              op->getLoc(), cast<TypedAttr>(attribute));
        }
        new_operands.push_back(attr_cst);
      }
    }

    // Create the TFR call op
    auto new_op = builder.create<CallOp>(