auto parent_module = entry_func->getParentOfType<ModuleOp>();
  auto versions_attr = parent_module->getAttr(kVersionsAttr);
  if (!versions_attr)
    return parent_module.emitError(CreateMissingAttributeMsg(kVersionsAttr));

  module_for_func.get().getOperation()->setAttr(kVersionsAttr, versions_attr);
  SymbolTable symbol_table(module_for_func.get());

  while (!referenced.empty()) {

  FetchOp fetch_op = graph_op.GetFetch();
  for (const Value fetch : fetch_op.getFetches()) {
    if (!mlir::isa<tf_executor::ControlType>(fetch.getType())) {
      fetch_op.emitError(absl::StrFormat(
          "Validation failed for the initializer function: %s. "
          "All initializer function's fetches should be "
          "tf_executor::ControlType. Got: %s.",

          op, /*name=*/"", /*ignore_unregistered_attrs=*/true);
  if (!node_def.ok()) {
    op->emitError("Failed to obtain TensorFlow NodeDef: " +
                  node_def.status().ToString());
    return false;
  }
  std::string node_def_str;
  if (!(*node_def)->SerializeToString(&node_def_str)) {
    op->emitError("Failed to serialize tensorflow NodeDef");
    return false;
  }

  if (resource_type.getSubtypes().size() != 1)
    return function.emitError()
           << "expects resource type of argument "
           << resource_arg.getArgNumber() << " to have one subtype, got "
           << resource_type;

  auto composite_ops = GetCompositeResourceUserNames(resource_arg);
  if (!composite_ops.empty())
    return function.emitError()
           << "expects users of resource argument "

                      int num_dims = 4) {
  int32_t input_zp_value;
  if (!GetSplatValue(input_zp, input_zp_value)) {
    emitError(loc,
              "zero point is expected to be a constant with a single value");
    return {};
  }
  if (strides.size() != num_dims || dilations.size() != num_dims) {
    emitError(loc,
              absl::StrFormat(
                  "strides and dilations are expected to be %d-element arrays",

        return func.emitError()
               << "failed to rename StableHLO function " << func.getSymName();
      }
    }
    if (is_main_func) {
      main_func_name = func.getSymNameAttr();
    }
    func->setAttr(kFromXlaCallModuleAttrName, builder.getUnitAttr());
  }
  if (!main_func_name) {
    return stablehlo_module.emitError()

                                    BlockArgument& block_arg) {
  // check that inputs length is same as num_replicas.
  if (inputs.size() != replicate.getN()) {
    return replicate.emitError()
           << "Expected numper of inputs (" << inputs.size()
           << ") to append to replicate to be num_replicas ("
           << replicate.getN() << ")";
  }

    if (!replication_info_attr)
      return metadata_op.emitError() << kBadReplicateInfoAttrMsg;

    auto replication_info_attr_str =
        mlir::dyn_cast<StringAttr>(replication_info_attr);
    if (!replication_info_attr_str ||
        replication_info_attr_str.getValue().empty())
      return metadata_op.emitError() << kBadReplicateInfoAttrMsg;

    // Remove `name` attribute.

    if (std::find_if(
            attr_to_op_map.begin(), attr_to_op_map.end(), [&](auto attr_op) {
              return std::get<0>(attr_op).getName() == attribute.getName();
            }) == attr_to_op_map.end()) {
      emitError(UnknownLoc::get(&context),
                "Could not find attribute: " + attribute.getName().str());
      return failure();
    }

    Operation* owner_op;
    for (const auto& [attr, val] : attr_to_op_map) {

  ReplicateOp::ensureTerminator(body, parser.getBuilder(), result.location);

  if (!llvm::hasSingleElement(body))
    return parser.emitError(loc) << "expects a single block region";

  Operation& terminator = body.front().back();
  if (!isa<ReturnOp>(terminator))
    return parser.emitError(loc) << "expects a tf_device.return terminator";

  // Get the results type from the terminator type inside the replicate,