def revert(self, model: Module) -> Module:
        """Convert the wrapped batchnorm layers back to regular batchnorm layers.

        Args:
            model: Reference to the current LightningModule

        Return:
            LightningModule with regular batchnorm layers that will no longer sync across processes.

        """
        # Code adapted from https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547
        # Original author: Kapil Yedidi (@kapily)
        converted_module = model
        if isinstance(model, torch.nn.modules.batchnorm.SyncBatchNorm):
            # Unfortunately, LayerSync does not store the original class - if it did
            # we could return the one that was originally created.
            converted_module = _BatchNormXd(
                model.num_features, model.eps, model.momentum, model.affine, model.track_running_stats
            )
            if model.affine:
                with torch.no_grad():
                    converted_module.weight = model.weight
                    converted_module.bias = model.bias
            converted_module.running_mean = model.running_mean
            converted_module.running_var = model.running_var
            converted_module.num_batches_tracked = model.num_batches_tracked
            if hasattr(model, "qconfig"):
                converted_module.qconfig = model.qconfig
        for name, child in model.named_children():
            converted_module.add_module(name, self.revert(child))
        del model
        return converted_module
Convert the wrapped batchnorm layers back to regular batchnorm layers.

        Args:
            model: Reference to the current LightningModule

        Return:
            LightningModule with regular batchnorm layers that will no longer sync across processes.

        
src/lightning/pytorch/plugins/layer_sync.py