TF_ShapeInferenceContext* ctx, const char* attr_name, c_type* val, \
      TF_Status* status) {                                               \
    TF_SetStatus(status, TF_OK, "");                                     \
    cc_type v;                                                           \
    auto* cc_ctx = reinterpret_cast<InferenceContext*>(ctx);             \
    Status s = cc_ctx->GetAttr(attr_name, &v);                           \

// `op_name` must outlive `op`.
void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,
                            TF_Status* s);
// `attr_name` must outlive `op`.
void TF_AbstractOpSetAttrType(TF_AbstractOp* op, const char* const attr_name,
                              TF_DataType value, TF_Status* s);

void TF_DeleteAbstractTensor(TF_AbstractTensor*);

  }
  tsl::Set_TF_Status_from_Status(s, tracing_op->SetOpName(op_name));
}

void TF_AbstractOpSetAttrType(TF_AbstractOp* op, const char* const attr_name,
                              TF_DataType value, TF_Status* s) {
  Status status =
      unwrap(op)->SetAttrType(attr_name, static_cast<DataType>(value));
  TF_SetStatus(s, static_cast<TF_Code>(status.code()),
               absl::StatusMessageAsCStr(status));
}

      } else {
        auto attr_name = compose_func.getArgAttrOfType<StringAttr>(
            arg.index(), kAttrArgumentNameAttr);
        auto attribute = op->getAttr(attr_name.getValue());
        if (!attribute) {
          attribute =
              compose_func.getArgAttr(arg.index(), kAttrArgumentDefaultAttr);
        }
        if (!attribute && attr_name.getValue() == "out_type") {

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {

    if (operand.index() >= call_op.getNumOperands()) {
      auto attr_name = signature.getArgAttrOfType<StringAttr>(
          operand.index(), kAttrArgumentNameAttr);
      auto attr_value =
          signature.getArgAttr(operand.index(), kAttrArgumentDefaultAttr);
      arg_attrs->push_back(
          rewriter.getNamedAttr(attr_name.getValue(), attr_value));
      continue;
    }

// array of length at least `max_values` (ideally set to total_size from
// TF_OpKernelConstruction_GetAttrSize(ctx, attr_name, &list_size,
// &total_size)).
TF_CAPI_EXPORT extern void TF_OpKernelConstruction_GetAttrTensorShape(
    TF_OpKernelConstruction* ctx, const char* attr_name, int64_t* dims,
    size_t num_dims, TF_Status* status);

TF_CAPI_EXPORT extern bool TF_IsRefInput(TF_OpKernelContext* ctx, int i,

    const bool use_const = entry.second;
    string attr_name = AvoidCPPKeywords(api_def_attr.rename_to());

    string attr_comment;
    if (!api_def_attr.description().empty()) {
      // TODO(keveman): Word wrap and indent this, to handle multi-line
      // descriptions.
      strings::StrAppend(&attr_comment, "* ", attr_name, ": ",
                         api_def_attr.description(), "\n");
    }

      }
    }

    auto check_i64_attr =
        [&](const std::string& attr_name) -> mlir::LogicalResult {
      if (!backend_config.contains(attr_name)) {
        return op.emitOpError()
               << "Missing " << attr_name << " attribute in backend_config";
      }
      auto attr = backend_config.getAs<IntegerAttr>(attr_name);
      if (!attr || !attr.getType().isInteger(64)) {
        return op.emitOpError()

      auto sizes = mlir::DenseI32ArrayAttr::get(context, {1, 1, 1, 0});
      auto attr_name =
          StringAttr::get(context, mlir::OpTrait::AttrSizedOperandSegments<
                                       void>::getOperandSegmentSizeAttr());
      attrs.push_back(NamedAttribute(attr_name, sizes));
    }

    // Insert fused operation right before the BiasAdd operation to guarantee