optimum_benchmark/backends/tensorrt_llm/backend.py [25:50]:
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    def load(self) -> None:
        self.logger.info("\t+ Creating backend temporary directory")
        self.tmpdir = TemporaryDirectory()

        if self.config.no_weights:
            self.logger.info("\t+ Creating no weights model")
            self.create_no_weights_model_slow()
            self.logger.info("\t+ Loading no weights model")
            self.load_model_with_no_weights()
        else:
            self.logger.info("\t+ Downloading pretrained model")
            self.download_pretrained_model()
            self.logger.info("\t+ Loading pretrained model")
            self.load_model_from_pretrained()

        try:
            self.tmpdir.cleanup()
        except Exception:
            shutil.rmtree(self.tmpdir.name, ignore_errors=True)

    def load_model_with_no_weights(self) -> None:
        original_model, self.config.model = self.config.model, self.no_weights_model_path.as_posix()
        self.load_model_from_pretrained()
        self.config.model = original_model

    def load_model_from_pretrained(self) -> None:
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optimum_benchmark/backends/vllm/backend.py [26:51]:
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    def load(self) -> None:
        self.logger.info("\t+ Creating backend temporary directory")
        self.tmpdir = TemporaryDirectory()

        if self.config.no_weights:
            self.logger.info("\t+ Creating no weights model")
            self.create_no_weights_model_slow()
            self.logger.info("\t+ Loading no weights model")
            self.load_model_with_no_weights()
        else:
            self.logger.info("\t+ Downloading pretrained model")
            self.download_pretrained_model()
            self.logger.info("\t+ Loading pretrained model")
            self.load_model_from_pretrained()

        try:
            self.tmpdir.cleanup()
        except Exception:
            shutil.rmtree(self.tmpdir.name, ignore_errors=True)

    def load_model_with_no_weights(self) -> None:
        original_model, self.config.model = self.config.model, self.no_weights_model_path.as_posix()
        self.load_model_from_pretrained()
        self.config.model = original_model

    def load_model_from_pretrained(self) -> None:
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