llvm::all_of(op->getResultTypes(), is_not_variant);
  };

  auto is_set_item_legal = [](Operation *op) {
    return op->hasAttr("resize_if_index_out_of_bounds") &&
           op->getAttr("resize_if_index_out_of_bounds")
               .cast<mlir::BoolAttr>()
               .getValue();
  };

  ConversionTarget target(*context);

    // 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;

      // avoid compilation failure when exporting "layouts" attribute of the
      // corresponding InfeedDequeueTupleOp to a graph node.
      data_and_token->setAttr("layout", op->getAttr("layouts"));
    }
    llvm::SmallVector<Value> results;
    results.reserve(result_types.size());
    for (const auto &idx_and_type : llvm::enumerate(result_types)) {

        which would previously raise an error. This will correspond to an
        attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only
        be accessed indirectly (e.g. through getattr and setattr). To set this
        up the user will first need to explicitly add the variable to the hparam
        object (e.g. "hparams.add_hparam(name='a.b', value=0.0)").