26:  _Attr_name[121:130],
	27:  _Attr_name[130:137],
	28:  _Attr_name[137:147],
	29:  _Attr_name[147:161],
	30:  _Attr_name[161:173],
	32:  _Attr_name[173:179],
	33:  _Attr_name[179:189],
	34:  _Attr_name[189:199],
	37:  _Attr_name[199:207],
	39:  _Attr_name[207:217],
	42:  _Attr_name[217:227],
	44:  _Attr_name[227:237],
	46:  _Attr_name[237:247],
	47:  _Attr_name[247:257],

	_ = x[attrStyle-3]
	_ = x[attrURL-4]
	_ = x[attrSrcset-5]
}

const _attr_name = "attrNoneattrScriptattrScriptTypeattrStyleattrURLattrSrcset"

var _attr_index = [...]uint8{0, 8, 18, 32, 41, 48, 58}

func (i attr) String() string {
	if i >= attr(len(_attr_index)-1) {
		return "attr(" + strconv.FormatInt(int64(i), 10) + ")"
	}
	return _attr_name[_attr_index[i]:_attr_index[i+1]]

  virtual Status SetAttrString(const char* attr_name, const char* data,
                               size_t length) = 0;
  virtual Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual Status SetAttrType(const char* attr_name, DataType value) = 0;

  return op_->SetAttrInt(attr_name, value);
}
Status SetAttrFloat(AbstractOperation* op_, const char* attr_name, float value,
                    ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, value);
  return op_->SetAttrFloat(attr_name, value);
}
Status SetAttrBool(AbstractOperation* op_, const char* attr_name, bool value,

    op_->node_builder.Attr(attr_name, value);
    return absl::OkStatus();
  }
  Status SetAttrBool(const char* attr_name, bool value) override {
    op_->node_builder.Attr(attr_name, value);
    return absl::OkStatus();
  }
  Status SetAttrType(const char* const attr_name, DataType value) override {
    if (!op_) {
      return Status(

  }
}

void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) {
  auto s = tensorflow::unwrap(op)->SetAttrInt(attr_name, value);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, float value) {
  auto s = tensorflow::unwrap(op)->SetAttrFloat(attr_name, value);
  if (!s.ok()) {

  Status SetAttrString(const char* attr_name, const char* data,
                       size_t length) override;
  Status SetAttrInt(const char* attr_name, int64_t value) override;
  Status SetAttrFloat(const char* attr_name, float value) override;
  Status SetAttrBool(const char* attr_name, bool value) override;
  Status SetAttrType(const char* attr_name, DataType value) override;

  forward_op_.attrs.Set(attr_name, value);
  return parent_op_->SetAttrFloat(attr_name, value);
}
Status TapeOperation::SetAttrBool(const char* attr_name, bool value) {
  forward_op_.attrs.Set(attr_name, value);
  return parent_op_->SetAttrBool(attr_name, value);
}
Status TapeOperation::SetAttrType(const char* attr_name, DataType value) {
  forward_op_.attrs.Set(attr_name, value);

  for (auto& attr : src->getAttrs()) {
    StringRef attr_name = attr.getName().strref();

    if (!registered.contains(attr_name) &&
        !(GetAllowedAttributes().contains(attr_name) ||
          GetOptionalAttributes().contains(attr_name))) {
      src->emitError("Denied unregistered attribute was found: " + attr_name);
      return failure();
    }
  }
  return success();
}

// with the key `attr_name` as the type provided by `attr_type`.
class GetCompositeAttributeAs<string attr_name, string attr_type>:
  NativeCodeCall<"$0.get(\"" # attr_name # "\").dyn_cast<" # attr_type # ">()">;

// Receives a composite DictionaryAttr and returns the value of the Attribute
// with the key `attr_name` as a DenseIntElementsAttr.
class GetAsVectorAttr<string attr_name>: