return op.emitOpError("requires input to have one resource");

  auto resource_type = input_type.getElementType().cast<TF::ResourceType>();
  auto subtypes = resource_type.getSubtypes();
  switch (subtypes.size()) {
    case 1:
      return VerifyShapeOperandAndResult(
          op, resource_type.getSubtypes().front(), op.getType());
    case 0:

    }

    TensorType resource_subtype = mlir::cast<TensorType>(value.getType());
    ResourceType resource_type =
        ResourceType::get({resource_subtype}, op.getContext());
    UnrankedTensorType new_resource_type =
        UnrankedTensorType::get(resource_type);

    Type refined_type = TypeMeet(resource.getType(), new_resource_type);
    if (refined_type == resource.getType()) continue;

            auto etype, ConvertToMlirTensorType(shape_proto, dtype, &builder));
        return mlir::UnrankedTensorType::get(mlir::TF::ResourceType::get(
            {mlir::cast<TensorType>(etype)}, builder.getContext()));
      } else {
        return mlir::UnrankedTensorType::get(
            mlir::TF::ResourceType::get(builder.getContext()));
      }
    } else if (auto shape = node.attrs().Find("_output_shapes")) {

    auto elementType = getElementTypeOrSelf(operand.getType());
    if (mlir::isa<mlir::TF::ResourceType>(elementType)) {
      return true;
    }
  }
  for (const auto& result : op->getResults()) {
    auto elementType = getElementTypeOrSelf(result.getType());
    if (mlir::isa<mlir::TF::ResourceType>(elementType)) {
      return true;
    }
  }
  return false;
}

    This pass promotes resource accesses in function(s) (by default, the main)
    to input arguments and outputs of the function(s).

    Two types of resources are supported:
    (1) A function argument of TF::ResourceType type (this pass).
    (2) A VarHandleOp in the function (tf-promote-var-handles-to-args).

    After the pass,

     . The function will have an input argument for each resource that is

    for (auto ba : body_block.getArguments()) {
      int arg_no = ba.getArgNumber();
      // Skip removing resources that are not read-only variables.
      if (getElementTypeOrSelf(ba.getType()).isa<TF::ResourceType>()) {
        bool has_read_only_variables = true;
        for (auto user : ba.getUsers()) {
          // Ternimator ops, for example, tfl::yield op, should be ignored since