for (auto type_and_name :
         llvm::zip(intermediate_types, kIntermediateNames)) {
      mlir::TypeAttr type_attr =
          mlir::TypeAttr::get(std::get<0>(type_and_name));
      auto named_attr =
          builder.getNamedAttr(std::get<1>(type_and_name), type_attr);
      op_state.addAttribute(named_attr.getName(), named_attr.getValue());
    }
  }
  return absl::OkStatus();
}

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;

  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  if (auto device_attr =

    // determined while going through quantization passes.
    OptionalAttr<TypeAttr>:$input_to_input_intermediate,
    OptionalAttr<TypeAttr>:$input_to_forget_intermediate,
    OptionalAttr<TypeAttr>:$input_to_cell_intermediate,
    OptionalAttr<TypeAttr>:$input_to_output_intermediate,
    OptionalAttr<TypeAttr>:$effective_hidden_scale_intermediate
  );

  let results = (outs AnyTensor:$output);

  if (tensorflow::IsRefType(dtype)) dtype = tensorflow::RemoveRefType(dtype);
  value->set_type(dtype);
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::TypeAttr& type, bool remove_ref_type,
                        AttrValue* value) {
  return ConvertAttribute(type.getValue(), remove_ref_type, value);
}

Status ConvertAttribute(const mlir::UnitAttr& attr, AttrValue* value) {

    builder.create<tf_saved_model::GlobalTensorOp>(
        NameLoc::get(builder.getStringAttr(name.str())),
        builder.getStringAttr(name), tensor_attr,
        TypeAttr::get(tensor_attr.getType()), builder.getUnitAttr());
  }

  return success();
}

}  // namespace

LogicalResult LiftVariables(ModuleOp module, Session* session) {

            res_types, res_attrs)))
      return failure();

    // Update the function type which has excluded the control args.
    func->setAttr("function_type", TypeAttr::get(rewriter.getFunctionType(
                                       arg_types, res_types)));

    // Update arg/result attributes.
    func.setAllArgAttrs(arg_attrs);
    func.setAllResultAttrs(res_attrs);

      return false;
    }
  }
  return true;
}

// Returns the tensor type created from the `shape_attr` and `type_attr`
// attributes.
Type GetType(Attribute shape_attr, Attribute type_attr) {
  auto shape = mlir::cast<tf_type::ShapeAttr>(shape_attr);
  auto type = mlir::cast<TypeAttr>(type_attr);
  if (shape.hasRank())
    return tensorflow::GetTypeFromTFTensorShape(shape.getShape(),

  return builder.getI32IntegerAttr(value);
}

static mlir::Attribute BuildTypeAttr(tflite::TensorType value,
                                     mlir::Builder builder) {
  return mlir::TypeAttr::get(ConvertElementType(value, builder));
}

static mlir::Attribute BuildTFL_AFAttr(tflite::ActivationFunctionType value,
                                       mlir::Builder builder) {

    type_attr = attr_value_pb2.AttrValue(type=types_pb2.DT_QINT8)
    if quantize:
      self.assertTrue(
          self._contains_op(output_graphdef, 'Const', 'dtype', type_attr)
      )
    else:
      self.assertFalse(
          self._contains_op(output_graphdef, 'Const', 'dtype', type_attr)
      )

  @parameterized.named_parameters(

    // Const op can have a result of dynamic shaped type (e.g. due to constant
    // folding), but we can still derive the shape of a constant tensor for
    // its attribute type.
    auto tensor_attr = mlir::cast<mlir::TypedAttr>(inst->getAttr("value"));
    llvm::ArrayRef<int64_t> shape_ref =
        mlir::cast<TensorType>(tensor_attr.getType()).getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;