optimum/intel/openvino/modeling_open_clip.py [257:296]:
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        }

        main_export(
            model_name_or_path=model_id,
            output=save_dir_path,
            task=task or cls.export_feature,
            subfolder=subfolder,
            revision=revision,
            cache_dir=cache_dir,
            token=token,
            local_files_only=local_files_only,
            force_download=force_download,
            trust_remote_code=trust_remote_code,
            ov_config=ov_config,
            library_name=cls._library_name,
            framework="pt",
            fn_get_submodels=fn_get_submodels,
            custom_architecture=True,
            custom_export_configs=custom_export_configs,
        )

        config.save_pretrained(save_dir_path)
        return cls._from_pretrained(
            model_id=save_dir_path,
            config=config,
            load_in_8bit=load_in_8bit,
            quantization_config=quantization_config,
            file_name=cls._xml_model_name,
            **kwargs,
        )

    @classmethod
    def _from_pretrained(
        cls,
        model_id: Union[str, Path],
        config: PretrainedConfig,
        token: Optional[Union[bool, str]] = None,
        revision: Optional[str] = None,
        force_download: bool = False,
        cache_dir: str = HUGGINGFACE_HUB_CACHE,
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optimum/intel/openvino/modeling_open_clip.py [382:421]:
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        }

        main_export(
            model_name_or_path=model_id,
            output=save_dir_path,
            task=task or cls.export_feature,
            subfolder=subfolder,
            revision=revision,
            cache_dir=cache_dir,
            token=token,
            local_files_only=local_files_only,
            force_download=force_download,
            trust_remote_code=trust_remote_code,
            ov_config=ov_config,
            library_name=cls._library_name,
            framework="pt",
            fn_get_submodels=fn_get_submodels,
            custom_architecture=True,
            custom_export_configs=custom_export_configs,
        )

        config.save_pretrained(save_dir_path)
        return cls._from_pretrained(
            model_id=save_dir_path,
            config=config,
            load_in_8bit=load_in_8bit,
            quantization_config=quantization_config,
            file_name=cls._xml_model_name,
            **kwargs,
        )

    @classmethod
    def _from_pretrained(
        cls,
        model_id: Union[str, Path],
        config: PretrainedConfig,
        token: Optional[Union[bool, str]] = None,
        revision: Optional[str] = None,
        force_download: bool = False,
        cache_dir: str = HUGGINGFACE_HUB_CACHE,
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