if (failed(max_unpooling.RewriteFunc())) {
      return signalPassFailure();
    }
  } else {
    // We will look for the `tfl_fusable_op` attribute and fuse as a custom op.
    DictionaryAttr dict_attr = attr.getAttrs();

    SmallVector<std::pair<StringRef, Attribute>, 4> attributes;
    bool tfl_fusable_op = false;
    for (auto attr_item : dict_attr) {
      // Push other attributes except the TFLFusableOp.

                                num_cores_per_replica)))
      return mlir::failure();

    // Copy TPUReplicateMetadata attributes to `tf_device.cluster`.
    cluster->setAttrs(
        cluster_metadata->second.getDictionary(cluster.getContext()));
    // Exclude `num_replicas` as cluster should be replicated if necessary.
    cluster->removeAttr(kNumReplicasAttr);
  }

      new_operands.emplace_back(operands[i]);
    }
  }
  auto new_while_op = builder.create<T>(while_op->getLoc(), new_operand_types,
                                        new_operands, while_op->getAttrs());
  int new_index = 0;
  for (int i = 0; i < num_operands; ++i) {
    if (!skip_indices.test(i)) {
      while_op->getResult(i).replaceAllUsesWith(
          new_while_op->getResult(new_index++));
    }
  }

  // {attribute_name, attribute_value} pair.
  llvm::SmallDenseMap<NamedAttribute, Operation*> attr_to_op_map;
  for (Operation* op : ops) {
    for (const NamedAttribute named_attr : op->getAttrs()) {
      attr_to_op_map.insert({named_attr, op});
    }
  }

  for (int idx : llvm::seq<int>(0, attributes.size())) {
    const NamedAttribute& attribute = attributes[idx];

      OperationState new_state(quantizing_op->getLoc(),
                               quantizing_op->getName().getStringRef(), inputs,
                               output_types, quantizing_op->getAttrs());
      for (int i = 0; i < quantizing_op->getNumRegions(); ++i) {
        new_state.addRegion();
      }
      Operation* quantized_op = rewriter.create(new_state);
      if (quantizing_op->getNumRegions() != 0) {

                        AttrValue* value) {
  TF_RETURN_IF_ERROR(ConvertAttribute(
      mlir::cast<mlir::FlatSymbolRefAttr>(attr.getName()), value));
  TF_RETURN_IF_ERROR(ConvertAttributes(attr.getAttrs().getValue(),
                                       /*attrs_to_ignore=*/{}, remove_ref_type,
                                       value->mutable_func()->mutable_attr()));
  return absl::OkStatus();
}

        encoding.getBounds()[i] == ShapedType::kDynamic) {
      return false;
    }
  }
  return true;
}

bool HasSymbolRefAttr(Operation* op) {
  for (const auto& attr : op->getAttrs()) {
    Attribute attr_value = attr.getValue();
    if (mlir::isa<SymbolRefAttr>(attr_value)) {
      return true;
    } else if (auto array_attr = mlir::dyn_cast<ArrayAttr>(attr_value)) {

  llvm::SmallVector<mlir::DictionaryAttr, 4> arg_attrs, res_attrs;
  main_op.getAllArgAttrs(arg_attrs);
  main_op.getAllResultAttrs(res_attrs);
  std::vector<std::string> sig_def_inputs =
      GetStringsFromDictionaryAttr(arg_attrs, kTfSavedModelIndexPathAttr);
  std::vector<std::string> sig_def_outputs =
      GetStringsFromDictionaryAttr(res_attrs, kTfSavedModelIndexPathAttr);

  }
  xla::ConvolutionDimensionNumbers dnums =
      ConvertConvolutionDimensionNumbers(dnums_input);

  SmallVector<NamedAttribute> converted_attrs;
  for (auto attr : op->getAttrs()) {
    if (attr.getName() == op.getFeatureGroupCountAttrName() ||
        attr.getName() == op.getBatchGroupCountAttrName()) {
      converted_attrs.push_back(attr);

    assert(funcs_for_cluster != tpu_funcs.end());
    assert(!funcs_for_cluster->second.empty());
    if (funcs_for_cluster->second.size() == 1) return false;
    for (NamedAttribute attr : op->getAttrs()) {
      auto symbol_ref = mlir::dyn_cast<FlatSymbolRefAttr>(attr.getValue());
      if (!symbol_ref) continue;
      func::FuncOp callee =
          symbol_table.lookup<func::FuncOp>(symbol_ref.getValue());