string c_base_type = attr_.base_type();
  if (attr_.base_type() == "type") {
    c_base_type = "DataType";
  } else if (attr_.base_type() == "shape") {
    c_base_type = "const PartialTensorShape";
  }

  // ... and wrap in a Span<> if it's a list.
  if (attr_.is_list()) {
    return absl::Substitute("absl::Span<$0>", c_base_type);
  } else {
    return c_base_type;
  }

  return attr_.full_type();
}

  }

  auto attrs = attr_.getAttrs();

  if (failed(HasIntegerArrayWithSize(&func_, attrs, "pool_size", 2))) {
    return failure();
  }

  if (failed(HasIntegerArrayWithSize(&func_, attrs, "strides", 2))) {
    return failure();
  }

  // Retrieves padding.
  auto padding = mlir::dyn_cast_or_null<StringAttr>(attrs.get("padding"));
  if (!padding) {

  }

  auto attrs = attr_.getAttrs();
  if (failed(HasIntAttr(func_, attrs, "max_detections")) ||
      failed(HasIntAttr(func_, attrs, "max_classes_per_detection")) ||
      failed(HasIntAttr(func_, attrs, "num_classes")) ||
      failed(HasFloatAttr(func_, attrs, "nms_score_threshold")) ||
      failed(HasFloatAttr(func_, attrs, "nms_iou_threshold")) ||

		$2 ~ /^(TASKSTATS|TS)_/ ||
		$2 ~ /^CGROUPSTATS_/ ||
		$2 ~ /^GENL_/ ||
		$2 ~ /^STATX_/ ||
		$2 ~ /^RENAME/ ||
		$2 ~ /^UBI_IOC[A-Z]/ ||
		$2 ~ /^UTIME_/ ||
		$2 ~ /^XATTR_(CREATE|REPLACE|NO(DEFAULT|FOLLOW|SECURITY)|SHOWCOMPRESSION)/ ||
		$2 ~ /^ATTR_(BIT_MAP_COUNT|(CMN|VOL|FILE)_)/ ||
		$2 ~ /^FSOPT_/ ||
		$2 ~ /^WDIO[CFS]_/ ||
		$2 ~ /^NFN/ ||
		$2 !~ /^NFT_META_IIFTYPE/ &&
		$2 ~ /^NFT_/ ||
		$2 ~ /^NF_NAT_/ ||

//
// Use Group to collect several key-value pairs under a single
// key on a log line, or as the result of LogValue
// in order to log a single value as multiple Attrs.
func Group(key string, args ...any) Attr {
	return Attr{key, GroupValue(argsToAttrSlice(args)...)}
}

func argsToAttrSlice(args []any) []Attr {
	var (
		attr  Attr
		attrs []Attr
	)
	for len(args) > 0 {

        extraAttributes.contains(attr2)
        !extraAttributes.contains(attr1)
    }

    def "collects extra attributes, two candidates"() {
        def attr1 = Attribute.of("foo", String)
        def attr2 = Attribute.of("bar", String)
        def attr3 = Attribute.of("baz", String)

        given:
        ImmutableAttributes[] candidates = [
            candidate(ImmutableMap.of(attr1, "v1", attr2, "v2")),

    device: The tensorflow device. eg. "/CPU:0"
    op_attrs: The tensorflow attributes excluding the func attrs.
    op_func_attrs: The func attrs.
    op_name: The tensorflow op name. eg. "tf.AddV2"

    Example:
      %out_ch = tfrt_fallback_async.createop(%in_ch) key(0) device("/CPU:0")
        "some.op"() {attr1 = value, attr2 = value2} {f_attr1 = "fn1", f_attr2 = "fn2"} num_args(1)
  }];

  let arguments = (ins

  for (auto attr : kQuantizationAxisAttrs) {
    if (defining_op->hasAttr(attr)) {
      return defining_op->getAttr(attr);
    }
  }
  // Not found.
  return rewriter.getI64IntegerAttr(-1);
}

LogicalResult CheckIfAttrIs8Bit(const std::string& attr, Operation* op,
                                bool& is_8_bit) {
  Type element_type;

      // will not use this representation.
      mlir::NamedAttrList attrs;
      for (const auto& func_attr : value.func().attr()) {
        TF_ASSIGN_OR_RETURN(auto attr,
                            ConvertAttributeValue(func_attr.second, builder));
        attrs.push_back(builder->getNamedAttr(func_attr.first, attr));
      }
      auto func_attrs = builder->getDictionaryAttr(attrs);

  // we cannot rely on node name; use an attribute instead.
  host_compute_builder.Attr(kXlaOriginalOutsideCompilationNodeName,
                            host_compute_builder.node_name());

  // Copy all attributes.
  for (const auto& attr : call_node->attrs()) {
    host_compute_builder.Attr(attr.first, attr.second);
  }

  // Populate tpu_core assignment.