LightningModule .load_from_checkpoint  requires specific model signature
I have a LightningModule similar to this:
class MyLightningModule(pl.LightningModule):
    def __init__(self, model: nn.Module, hparams: argparse.Namespace):
          init code here
    @staticmethod
    def add_task_specific_args(parent_parser: Optional[Union[argparse.ArgumentParser,list]] = None):
          arguments added here
Unfortunately this type of signature does not seem to be supported by
.load_from_checkpoint(checkpoint_path=my/ckpt/path)
see here:

  
    
      pytorch-lightning/pytorch_lightning/core/saving.py
    
    
         Line 192
      in
      cdbddbe
    
  
  
    

        
          
           model = cls(**_cls_kwargs) 
        
    
  


at least I get the following Error:
File "/opt/conda/envs/MyEnv/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 192, in _load_model_state
    model = cls(**_cls_kwargs)
TypeError: __init__() missing 2 required positional arguments: 'model' and 'hparams'
So basically what I am wondering, I would like to keep the "model" separate from the "training/experiment" and also use Lightning for saving/loading weights. How do I achieve this?