src/transformers/integrations.py/MLflowCallback/on_log
class MLflowCallback:    def on_log(self, args, state, control, logs, model=None, **kwargs):
        if not self._initialized:
            self.setup(args, state, model)
        if state.is_world_process_zero:
            for k, v in logs.items():
                if isinstance(v, (int, float)):
                    self._ml_flow.log_metric(k, v, step=state.global_step)
                else:
                    logger.warning(
                        f"Trainer is attempting to log a value of "
                        f'"{v}" of type {type(v)} for key "{k}" as a metric. '
                        f"MLflow's log_metric() only accepts float and "
                        f"int types so we dropped this attribute."
                    )