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training/docs/modules/losses.rst

@@ -111,9 +111,9 @@ By default, only `all` is kept in the normalised space and scaled.
 
 Additionally, you can define your own loss function by subclassing
 ``BaseLoss`` and implementing the ``forward`` method, or by subclassing
-``BaseLoss`` and implementing the ``calculate_difference``
-function. The latter abstracts the scaling, and node weighting, and
-allows you to just specify the difference calculation.
+``BaseLoss`` and implementing the ``calculate_difference`` function. The
+latter abstracts the scaling, and node weighting, and allows you to just
+specify the difference calculation.
 
 .. code:: python
 

training/src/anemoi/training/losses/base.py

@@ -105,13 +105,13 @@ def scale(
 
         scaler = scaler.expand_as(x)
         return x[subset_indices] * scaler[subset_indices]
-    
+
     def reduce(self, out: torch.Tensor, squash: bool = True) -> torch.Tensor:
         if squash:
             out = self.avg_function(out, dim=-1)
 
         return self.sum_function(out, dim=(0, 1, 2))
-    
+
     def forward(
         self,
         pred: torch.Tensor,

training/src/anemoi/training/losses/scalers/base_scaler.py

@@ -78,7 +78,7 @@ def normalise(self, values: np.ndarray) -> np.ndarray:
 
     def get_scaling(self) -> SCALER_DTYPE:
         """Get scaler.
-        
+
         Returns
         -------
         scale_dims : tuple[int]