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>(

    std::string tmp_dir{};
    if (!env_->LocalTempFilename(&tmp_dir)) {
      return absl::InternalError("Failed to create temp file.");
    }

    TF_CHECK_OK(env_->CreateDir(tmp_dir));
    return tmp_dir;
  }

  // Parses `module_op_str` to create a `ModuleOp`. Checks whether the created
  // module op is valid.
  mlir::OwningOpRef<mlir::ModuleOp> ParseModuleOpString(
      const absl::string_view module_op_str) {

  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,

  // (a) read-only. These reads can be replaced by a hoisted read created
  //        before the WhileOp (similar to if and case).
  // (b) written: since the value is written in the loop (which can only in
  //        loop body, all these will become loop variables. Since all resource
  //        variables are removed from the loop variabled during
  //        canonicalizationW, we need to create new operand/result slots. The

}

// This is a basic test to make sure the C++ gradient code can handle while
// loops created by the C API (which calls the C++ API under the hood). There
// are more while loop gradient tests in cc/framework/while_gradients_test.cc.
TEST_F(CApiWhileLoopTest, Gradients) {
  Init(1);

  // Create loop: while (i < 10) i += 1
  TF_Operation* ten = ScalarConst(10, params_->cond_graph, s_);
  TF_Operation* less_than =

  if (only_one_return_value) {
    return_types.resize(1);
  }

  auto type = FunctionType::get(region.getContext(), input_types, return_types);

  // Create new function and extract region body into the function.
  auto outlined_func = builder.create<func::FuncOp>(loc, name, type);
  Region& func_region = outlined_func.getBody();
  func_region.takeBody(region);
  Block& first_block = func_region.front();