s only attempted if the previous one is
                finished. A last push is made with the final model at the end of training.
                - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named
                last-checkpoint, allowing you to resume training easily with
                `trainer.train(resume_from_checkpoint="last-checkpoint")`.
                - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the
                  output
                folder (so you will get one checkpoint folder per folder in your final repository)

            token (`str`, *optional*):
                The token to use to push the model to the Hub. Will default to the token in the cache folder obtained
                with `huggingface-cli login`.
            private_repo (`bool`, *optional*, defaults to `False`):
                If True, the Hub repo will be set to private.
            always_push (`bool`, *optional*, defaults to `False`):
                Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not
                finished.

        Example:

        ```py
        >>> from transformers import TrainingArguments

        >>> args = TrainingArguments("working_dir")
        >>> args = args.set_push_to_hub("me/awesome-model")
        >>> args.hub_model_id
        'me/awesome-model'
        ```
        TN)