fixing: identation error

lightorch/training/supervised.py

@@ -1,5 +1,5 @@
 from lightning.pytorch import LightningModule
-from typing import Union, Sequence, Any, Tuple, Dict
+from typing import Union, Sequence, Any, Dict, List
 from torch import Tensor, nn
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
@@ -38,7 +38,15 @@ def interval(algo: LRScheduler) -> str:
     else:
         return "epoch"
 
-class Module(LightningModule): """ init: triggers: Dict[str, Dict[str, float]] -> This is an interpretative implementation for grouped optimization where the parameters are stored in groups given a "trigger", namely, as trigger parameters you can put a string describing the beginning of the parameters to optimize in a group. optimizer: str | Optimizer -> Name of the optimizer or an Optimizer instance. scheduler: str | LRScheduler -> Name of the scheduler or a Scheduler instance. scheduler_kwargs: Dict[str, Any] -> Arguments of the scheduler. gradient_clip_algorithm: str -> Gradient clip algorithm [value, norm]. gradient_clip_val: float -> Clipping value. """
+class Module(LightningModule): 
+    """ 
+    init: triggers: Dict[str, Dict[str, float]] -> This is an interpretative implementation for grouped optimization where the parameters are stored in groups given a "trigger", namely, as trigger parameters you can put a string describing the beginning of the parameters to optimize in a group. 
+    optimizer: str | Optimizer -> Name of the optimizer or an Optimizer instance. 
+    scheduler: str | LRScheduler -> Name of the scheduler or a Scheduler instance. 
+    scheduler_kwargs: Dict[str, Any] -> Arguments of the scheduler. 
+    gradient_clip_algorithm: str -> Gradient clip algorithm [value, norm]. 
+    gradient_clip_val: float -> Clipping value. 
+    """
     def __init__(
         self,
         *,
@@ -100,7 +108,7 @@ def __init__(
     def loss_forward(self, batch: Tensor, idx: int) -> Dict[str, Union[Tensor, float]]:
         raise NotImplementedError("Should have defined loss_forward method.")
 
-    def training_step(self, batch: Tensor, idx: int) -> Tensor:
+    def training_step(self, batch: Tensor, idx: int) -> Union[float, Tensor]:
         kwargs = self.loss_forward(batch, idx)
         return self._compute_training_loss(**kwargs)
 
@@ -109,7 +117,7 @@ def validation_step(self, batch: Tensor, idx: int) -> None:
         kwargs = self.loss_forward(batch, idx)
         return self._compute_valid_metrics(**kwargs)
 
-    def _compute_training_loss(self, **kwargs) -> Union[Tensor, Sequence[Tensor]]:
+    def _compute_training_loss(self, **kwargs) -> Union[float, Tensor]:
         args = self.criterion(**kwargs)
         self.log_dict(
             {f"Training/{k}": v for k, v in zip(self.criterion.labels, args)},
@@ -132,12 +140,12 @@ def _compute_valid_metrics(self, **kwargs) -> None:
             True,
         )
 
-    def get_param_groups(self) -> Tuple:
+    def get_param_groups(self) -> List[Dict[str, Union[nn.Module, List[float]]]]:
         """
         Given a list of "triggers", the param groups are defined.
         """
         if self.triggers is not None:
-            param_groups: Sequence[Dict[str, Sequence[nn.Module]]] = [
+            param_groups: Sequence[Dict[str, List[nn.Module]]] = [
                 defaultdict(list) for _ in range(len(self.triggers))
             ]
             # Update the model parameters per group and finally add the
@@ -181,7 +189,7 @@ def _configure_scheduler(self, optimizer: Optimizer) -> LRScheduler:
         else:
             return self.scheduler(optimizer)
 
-    def configure_optimizers(self) -> Union[Optimizer, Sequence[Optimizer]]:
+    def configure_optimizers(self) -> Dict[str, Union[Optimizer, Dict[str, Union[float, int, LRScheduler]]]]:
         optimizer = self._configure_optimizer()
         if self.scheduler is not None:
             scheduler = self._configure_scheduler(optimizer)