Improve docs for to_dataloader

pytorch_forecasting/data.py

@@ -1191,6 +1191,23 @@ def to_dataloader(self, train: bool = True, batch_size: int = 64, **kwargs) -> D
         Args:
             train (bool, optional): if dataloader is used for training or prediction
                 Will shuffle and drop last batch if True. Defaults to True.
+            batch_size (int): batch size for training model. Defaults to 64.
+            **kwargs: additional arguments to ``DataLoader()``
+            
+            
+        Examples:
+        
+            To samples for training:
+            
+            .. code-block:: python
+                
+                from torch.utils.data import WeightedRandomSampler
+                
+                # length of probabilties for sampler have to be equal to the length of the index
+                probabilities = np.sqrt(1 + data.loc[dataset.index, "target"])
+                sampler = WeightedRandomSampler(probabilities, len(probabilities))
+                dataset.to_dataloader(train=True, sampler=sampler, shuffle=False)
+                
 
         Returns:
             DataLoader: dataloader that returns Tuple.
@@ -1208,15 +1225,16 @@ def to_dataloader(self, train: bool = True, batch_size: int = 64, **kwargs) -> D
                 Second entry is target
         )
         """
-        return DataLoader(
-            self,
+        default_kwargs = dict(
             shuffle=train,
             drop_last=train and len(self) > batch_size,
             collate_fn=self._collate_fn,
             batch_size=batch_size,
-            **kwargs,
         )
 
+        default_kwargs.update(kwargs)
+        return DataLoader(self, **default_kwargs,)
+
     def get_index(self) -> pd.DataFrame:
         """
         Data index / order in which items are returned in train=False mode by dataloader.