fix rendering part 2

pina/solver/supervised.py

@@ -52,13 +52,14 @@ class SupervisedSolver(SingleSolverInterface):
         :param AbstractProblem problem: The problem to be solved.
         :param torch.nn.Module model: The neural network model to be used.
         :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 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 bool use_lt: If ``True``, the solver uses LabelTensors as input.
@@ -86,10 +87,11 @@ class SupervisedSolver(SingleSolverInterface):
         """
         The optimization cycle for the solvers.
 
-        :param list[tuple[str, dict]] batch: The batch element in the dataloader.
-        :return: The computed loss for the all conditions in the batch, casted
-            to a subclass of `torch.Tensor`. It should return a dict containing
-            the condition name and the associated scalar loss.
+        :param list[tuple[str, dict]] batch: A batch of data. Each element is a
+            tuple containing a condition name and a dictionary of points.
+        :return: The losses computed for all conditions in the batch, casted
+            to a subclass of :class:`torch.Tensor`. It should return a dict
+            containing the condition name and the associated scalar loss.
         :rtype: dict
         """
         condition_loss = {}