btw I monkey patched ignite’s function global_step_from_engine to print the iteration and passed the modified function to the ClearMLLogger.attach_output_handler(…, global_step_transform=patched_global_step_from_engine(engine)) . It prints the correct iteration number when calling ClearMLLogger.OutputHandler.__ call__ .
` def call(self, engine: Engine, logger: ClearMLLogger, event_name: Union[str, Events]) -> None:

    if not isinstance(logger, ClearMLLogger):
        raise RuntimeError("Handler OutputHandler works only with ClearMLLogger")

    metrics = self._setup_output_metrics(engine)

    global_step = self.global_step_transform(engine, event_name)  # type: ignore[misc]

    if not isinstance(global_step, int):
        raise TypeError(
            f"global_step must be int, got {type(global_step)}."
            " Please check the output of global_step_transform."
        )

    for key, value in metrics.items():
        if isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0:
            logger.clearml_logger.report_scalar(title=self.tag, series=key, iteration=global_step, value=value)
        elif isinstance(value, torch.Tensor) and value.ndimension() == 1:
            for i, v in enumerate(value):
                logger.clearml_logger.report_scalar(
                    title=f"{self.tag}/{key}", series=str(i), iteration=global_step, value=v.item()
                )
        else:
            warnings.warn(f"ClearMLLogger output_handler can not log metrics value type {type(value)}") `I don’t understand how it can log a wrong iteration if the  ` global_step `  var has the right value in this function