list->add_s(nested_value.s());
          break;
        case AttrValue::kType:
          list->add_type(nested_value.type());
          break;
        case AttrValue::kShape:
          *list->add_shape() = nested_value.shape();
          break;
        default:
          return errors::Unimplemented("Unhandled nested attribute!");
      }
    } else if (auto attr = mlir::dyn_cast<mlir::ElementsAttr>(a)) {

                     .Attr("dtype", data_type)
                     .Attr("value", tensor);
  // Create a bad output shape attr.
  AttrValue shape_attr;
  TensorShapeProto* shape_proto = shape_attr.mutable_list()->add_shape();
  shape_proto->add_dim()->set_size(1);
  builder.Attr("_output_shapes", shape_attr);

  TF_RETURN_IF_ERROR(builder.Finalize(&node));

  return CreateSingleOpGraph(node, {}, {DataType::DT_INT32});
}

                       AttrValueMap* values) {
  AttrValue value;
  auto& shape_list = *value.mutable_list();
  for (const std::optional<llvm::ArrayRef<int64_t>>& shape : shapes) {
    TensorShapeProto& tshape = *shape_list.add_shape();
    if (shape.has_value()) {
      for (int64_t dim : *shape) {
        tshape.add_dim()->set_size(mlir::ShapedType::isDynamic(dim) ? -1 : dim);
      }
    } else {
      tshape.set_unknown_rank(true);
    }

        handle_dtypes_attr.mutable_list()->add_type(dtype);

        SetTensorShapeProto(subtype,
                            handle_shapes_attr.mutable_list()->add_shape());
      }

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(

        handle_dtypes_attr.mutable_list()->add_type(dtype);

        SetTensorShapeProto(subtype,
                            handle_shapes_attr.mutable_list()->add_shape());
      }

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(

        builder.addEscape(c, "\uFFFD");
      }
    }

    // Build the content escaper first and then add quote escaping for the
    // general escaper.
    builder.addEscape('&', "&");
    builder.addEscape('<', "&lt;");
    builder.addEscape('>', "&gt;");
    XML_CONTENT_ESCAPER = builder.build();
    builder.addEscape('\'', "&apos;");
    builder.addEscape('"', "&quot;");

        builder.addEscape(c, "\uFFFD");
      }
    }

    // Build the content escaper first and then add quote escaping for the
    // general escaper.
    builder.addEscape('&', "&");
    builder.addEscape('<', "&lt;");
    builder.addEscape('>', "&gt;");
    XML_CONTENT_ESCAPER = builder.build();
    builder.addEscape('\'', "&apos;");
    builder.addEscape('"', "&quot;");

        const InferredShape& arg_shape = it->second;
        shape_inference::InferenceContext* context =
            shape_refiner->GetContext(n);

        if (arg_shape.handle_type != DT_INVALID) {
          shape_inference::ShapeHandle handle;
          TF_RETURN_IF_ERROR(context->MakeShapeFromPartialTensorShape(
              arg_shape.handle_shape, &handle));

  @CanIgnoreReturnValue
  public CharEscaperBuilder addEscape(char c, String r) {
    map.put(c, checkNotNull(r));
    if (c > max) {
      max = c;
    }
    return this;
  }

  /** Add multiple mappings at once for a particular index. */
  @CanIgnoreReturnValue
  public CharEscaperBuilder addEscapes(char[] cs, String r) {
    checkNotNull(r);
    for (char c : cs) {
      addEscape(c, r);
    }
    return this;
  }

};

struct VariantSubTypeBuilder {
  typedef VariantSubType Table;
  ::flatbuffers::FlatBufferBuilder &fbb_;
  ::flatbuffers::uoffset_t start_;
  void add_shape(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape) {
    fbb_.AddOffset(VariantSubType::VT_SHAPE, shape);
  }
  void add_type(tflite::TensorType type) {