Number of update steps between two logs if `strategy="steps"`.
            level (`str`, *optional*, defaults to `"passive"`):
                Logger log level to use on the main process. Possible choices are the log levels as strings: `"debug"`,
                `"info"`, `"warning"`, `"error"` and `"critical"`, plus a `"passive"` level which doesn't set anything
                and lets the application set the level.
            report_to (`str` or `List[str]`, *optional*, defaults to `"none"`):
                The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
                `"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report
                to all integrations installed, `"none"` for no integrations.
            first_step (`bool`, *optional*, defaults to `False`):
                Whether to log and evaluate the first `global_step` or not.
            nan_inf_filter (`bool`, *optional*, defaults to `True`):
                Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is
                `nan` or `inf` is filtered and the average loss of the current logging window is taken instead.

                <Tip>

                `nan_inf_filter` only influences the logging of loss values, it does not change the behavior the
                gradient is computed or applied to the model.

                </Tip>

            on_each_node (`bool`, *optional*, defaults to `True`):
                In multinode distributed training, whether to log using `log_level` once per node, or only on the main
                node.
            replica_level (`str`, *optional*, defaults to `"passive"`):
                Logger log level to use on replicas. Same choices as `log_level`

        Example:

        ```py
        >>> from transformers import TrainingArguments

        >>> args = TrainingArguments("working_dir")
        >>> args = args.set_logging(strategy="steps", steps=100)
        >>> args.logging_steps
        100
        ```
        r