projection_type,
                                         layer_norm_scale_type};
  auto func_type = builder->getFunctionType(input_types, output_type);

  auto func = func::FuncOp::create(
      mlir::NameLoc::get(builder->getStringAttr("fused_func")), "fused_func",
      func_type, {});
  func.addEntryBlock();

  std::vector<std::string> attributes;
  if (ln) {

  for (auto attr : op.getPlatforms().getAsRange<StringAttr>()) {
    platforms.push_back(attr.getValue().str());
  }
  TF_ASSIGN_OR_RETURN(
      auto loader,
      tensorflow::XlaCallModuleLoader::Create(
          context, static_cast<int>(op.getVersion()), op.getModule().str(),
          std::move(disabled_checks), std::move(platforms),
          /*num_invocation_args=*/op.getArgs().size(),

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

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) {

  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);

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>(

  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();