while (
      llvm::isa_and_nonnull<IdentityOp, IdentityNOp>(value.getDefiningOp())) {
    value = value.getDefiningOp()->getOperand(
        mlir::cast<OpResult>(value).getResultNumber());
  }

  Operation* op = value.getDefiningOp();
  if (!llvm::isa_and_nonnull<BatchDatasetV2Op, MapDatasetOp, RepeatDatasetOp,

  // None type is allowed to represent unspecified operands.
  if (mlir::isa<NoneType>(value.getType())) return true;

  auto type = mlir::dyn_cast<TensorType>(value.getType());
  if (!type) {
    if (auto op = value.getDefiningOp()) {
      error_handler.emitError()
          << '\'' << op << "' should produce value of tensor type instead of "
          << value.getType();
      return false;
    }

  auto map_fn = [](Value value) -> int {
    Value parent = value;
    while (true) {
      if (auto identity = parent.getDefiningOp<TF::IdentityOp>()) {
        parent = identity.getInput();
      } else if (auto set_item =
                     parent.getDefiningOp<TF::TensorListSetItemOp>()) {
        parent = set_item.getInputHandle();
      } else {
        break;
      }
    }

  for (Value fetch_operand : fetch.getOperands()) {
    if (mlir::isa<mlir::tf_executor::ControlType>(fetch_operand.getType())) {
      Operation* defining_op =
          GetIslandInnerOpOrSelf(fetch_operand.getDefiningOp());
      auto node_it = nodes_.find(defining_op);
      TF_RET_CHECK(node_it != nodes_.end());
      control_ret_nodes->insert(node_it->second);
    }
  }
  return absl::OkStatus();
}

                         input_shape_attr.getValues<int32_t>().begin(),
                         input_shape_attr.getValues<int32_t>().end());
    } else {
      auto pack = op.getInputSizes().template getDefiningOp<TF::PackOp>();
      if (!pack || pack.getAxis() != 0) return failure();
      auto pack_ty = mlir::dyn_cast<RankedTensorType>(pack.getType());
      if (!pack_ty || pack_ty.getRank() != 1) return failure();