def METHOD_NAME(self):
    # internal results are not supported by this tool but needed by externals
    doc = {"internal_results": []}
    if self.args["db_metadata"] is not None:
        for kv in self.args["db_metadata"]:
            parts = kv.split(":")
            if len(parts) < 2: continue
            elif len(parts) == 2:
                key, value = parts
            else:
                key = parts[0]
                value = ":".join(parts[1:])
            doc[key] = value
    doc["final_results"] = {}
    doc["execution_date"] = self.exec_date
    doc["client_version"] = "2.6"
    doc["model_name"] = self.args["model_name"]
    doc["model_version"] = self.args["model_version"]
    if "model" not in doc: doc["model"] = self.args["model_name"]
    if "batchsize" not in doc: doc["batchsize"] = "-".join(map(str, self.args["bs"]))
    if "iterations" not in doc: doc["iterations"] = self.args["steps_number"]
    if "concurr" not in doc: doc["concurr"] = self.args["concurrency"]
    if "duration" not in doc: doc["duration"] = self.args["duration"]
    if "window" not in doc: doc["window"] = self.args["window"]
    if "warmup" not in doc: doc["warmup"] = self.args["warmup"]
    for karg, varg in self.args.items():
        if karg == "db_metadata": continue
        if karg == "db_endpoint": continue
        if karg == "quantile_list": continue
        if isinstance(varg, (str, int, float)):
            doc[f"opt_{karg}"] = varg
        elif isinstance(varg, (list, tuple)):
            val = "_".join(map(str, varg))
            doc[f"opt_{karg}"] = val
    return doc