if (dict_attr) {
    TF_RET_CHECK(mlir::isa<mlir::StringAttr>(dict_attr.get("inputs")))
        << "inputs missing in entry function attribute";
    TF_RET_CHECK(mlir::isa<mlir::StringAttr>(dict_attr.get("outputs")))
        << "outputs missing in entry function attribute";
    mlir::cast<mlir::StringAttr>(dict_attr.get("inputs"))
        .getValue()

      block_arg.getLoc(),
      DenseIntElementsAttr::get(
          RankedTensorType::get({1, num_replicas}, builder.getIntegerType(32)),
          group_assignment_val));

  StringAttr reduce_op = builder.getStringAttr("Add");
  StringAttr mode = builder.getStringAttr("CrossReplica");
  return builder.create<XlaAllReduceOp>(block_arg.getLoc(), block_arg.getType(),

// Constraint that attribute is string with value either "SAME" or "VALID"
def IsSameOrValid : AttrConstraint<
    CPred<"$_self.cast<StringAttr>().getValue() == \"SAME\" || " #
          "$_self.cast<StringAttr>().getValue() == \"VALID\"">,
    "'SAME' or 'VALID' paddings">;

def TFL_GetMirrorPaddingType : NativeCodeCall<
    "mlir::TFL::MirrorPaddingTypeAttr::get($_builder.getContext(), " #

}

mlir::LogicalResult GetTopology(mlir::tf_device::ClusterOp cluster,
                                std::string& topology) {
  mlir::StringAttr topology_attr =
      cluster->getAttrOfType<mlir::StringAttr>(tensorflow::kTopologyAttr);
  if (topology_attr) {
    topology = topology_attr.getValue();
    return mlir::success();
  } else {
    return cluster.emitOpError(