fix(losses): add missing docstring entries for default parameters

monai/losses/cldice.py

@@ -126,8 +126,8 @@ class SoftclDiceLoss(_Loss):
     def __init__(self, iter_: int = 3, smooth: float = 1.0) -> None:
         """
         Args:
-            iter_: Number of iterations for skeletonization
-            smooth: Smoothing parameter
+            iter_: Number of iterations for skeletonization. Defaults to 3.
+            smooth: Smoothing parameter. Defaults to 1.0.
         """
         super().__init__()
         self.iter = iter_
@@ -160,9 +160,9 @@ class SoftDiceclDiceLoss(_Loss):
     def __init__(self, iter_: int = 3, alpha: float = 0.5, smooth: float = 1.0) -> None:
         """
         Args:
-            iter_: Number of iterations for skeletonization
-            smooth: Smoothing parameter
-            alpha: Weighing factor for cldice
+            iter_: Number of iterations for skeletonization. Defaults to 3.
+            alpha: Weighing factor for cldice. Defaults to 0.5.
+            smooth: Smoothing parameter. Defaults to 1.0.
         """
         super().__init__()
         self.iter = iter_

monai/losses/hausdorff_loss.py

@@ -54,6 +54,7 @@ def __init__(
     ) -> None:
         """
         Args:
+            alpha: the exponent to transform the distance when computing the loss. Defaults to 2.0.
             include_background: if False, channel index 0 (background category) is excluded from the calculation.
                 if the non-background segmentations are small compared to the total image size they can get overwhelmed
                 by the signal from the background so excluding it in such cases helps convergence.