fix rendering part 2

pina/solver/physic_informed_solver/rba_pinn.py

@@ -59,11 +59,11 @@ class RBAPINN(PINN):
     .. seealso::
         **Original reference**: Sokratis J. Anagnostopoulos, Juan D. Toscano,
         Nikolaos Stergiopulos, and George E. Karniadakis.
-        "Residual-based attention and connection to information 
-        bottleneck theory in PINNs".
+        *Residual-based attention and connection to information 
+        bottleneck theory in PINNs.*
         Computer Methods in Applied Mechanics and Engineering 421 (2024): 116805
-        DOI: `10.1016/
-        j.cma.2024.116805 <https://doi.org/10.1016/j.cma.2024.116805>`_.
+        DOI: `10.1016/j.cma.2024.116805
+        <https://doi.org/10.1016/j.cma.2024.116805>`_.
     """
 
     def __init__(
@@ -82,15 +82,16 @@ class RBAPINN(PINN):
 
         :param AbstractProblem problem: The problem to be solved.
         :param torch.nn.Module model: The neural network model to be used.
-        :param torch.optim.Optimizer optimizer: The optimizer to be used.
-            If `None`, the Adam optimizer is used. Default is ``None``.
-        :param torch.optim.LRScheduler scheduler: Learning rate scheduler.
-            If `None`, the constant learning rate scheduler is used.
+        param Optimizer optimizer: The optimizer to be used.
+            If `None`, the :class:`torch.optim.Adam` optimizer is used.
             Default is ``None``.
+        :param Scheduler scheduler: Learning rate scheduler.
+            If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR`
+            scheduler is used. Default is ``None``.
         :param WeightingInterface weighting: The weighting schema to be used.
             If `None`, no weighting schema is used. Default is ``None``.
         :param torch.nn.Module loss: The loss function to be minimized.
-            If `None`, the Mean Squared Error (MSE) loss is used.
+            If `None`, the :class:`torch.nn.MSELoss` loss is used.
             Default is `None`.
         :param float | int eta: The learning rate for the weights of the
             residuals. Default is ``0.001``.
@@ -147,7 +148,7 @@ class RBAPINN(PINN):
         :param LabelTensor loss_value: the tensor of pointwise losses.
         :raises RuntimeError: If the loss reduction is not ``mean`` or ``sum``.
         :return: The computed scalar loss.
-        :rtype LabelTensor
+        :rtype: LabelTensor
         """
         if self.loss.reduction == "mean":
             ret = torch.mean(loss_value)