using ::mlir::TensorType;
using ::mlir::tf_saved_model::AssetOp;
using ::mlir::tf_saved_model::GlobalTensorOp;
using ::mlir::tf_saved_model::kTfSavedModelExportedNamesAttr;
using ::mlir::tf_saved_model::kTfSavedModelIndexPathAttr;
using ::mlir::tf_saved_model::kTfSavedModelInitializerInitType;
using ::mlir::tf_saved_model::kTfSavedModelInitializerRestoreType;
using ::mlir::tf_saved_model::kTfSavedModelInitializerTypeAttr;

      self-contained.
      For example, this would transform

      "tf_saved_model.global_tensor"() { sym_name = "v" ... }
      func @f(%arg0 {tf_saved_model.bound_input = @v}) {
        %1 = "tf.ReadVariableOp"(%arg0)
        ...
      }

      to

      func @f(%arg0 {tf_saved_model.bound_input = @v}) {
        %0 = "tf.VarHandleOp"(sym_name = "v")
        %1 = "tf.ReadVariableOp"(%0)

    pool_params.computed.padding = TfLitePaddingValues{0, 0, 0, 0};
    return pool_params;
  }

  return std::nullopt;
}

namespace {

using ::mlir::tf_saved_model::kTfSavedModelExportedNamesAttr;
using ::mlir::tf_saved_model::kTfSavedModelIndexPathAttr;

// Helper struct that wraps inputs/outputs of a single SignatureDef.
struct SignatureDefData {
  // Note, we are using maps here to make order deterministic

// -----

// Tests about we create the dependency PwStreamResults `start` and `end`
// within in XlaCallModule
func.func @_pws_program(%arg0: tensor<i32> {tf_saved_model.index_path = ["arg0"]}) -> (tensor<i32> {tf_saved_model.index_path = ["result0"]}, tensor<i32> {tf_saved_model.index_path = ["result1"]}) attributes {pws.program_id = 4722582128360897113 : i64, tf.entry_function = {}} {
  // expected-remark@above {{ID: 7}}