docs: add docstring to WebDataloader, typehints to _init_modality

src/modalities/dataloader/dataloader.py

@@ -289,6 +289,8 @@ def __len__(self) -> int:
 
 
 class WebDataLoader(DataLoaderIF):
+    """WebDataLoader is a custom DataLoader class that wraps the webdataset.WebLoader class."""
+
     def __init__(
         self,
         dataloader_tag: str,
@@ -299,6 +301,18 @@ def __init__(
         pin_memory: bool = False,
         drop_last: bool = False,
     ):
+        """Initializes WebDataLoader, which is a wrapper for webdataset.WebLoader.
+
+        Args:
+            dataloader_tag (str): The tag for the dataloader.
+            dataset (Dataset[T_co]): The dataset to load the data from.
+            batch_size (Optional[int], optional): The batch size. Defaults to 1.
+            num_workers (int, optional): The number of worker processes to use for data loading. Defaults to 0.
+            collate_fn (Optional[_collate_fn_t], optional): The function used to collate the data samples.
+              Defaults to None.
+            pin_memory (bool, optional): Flag indicating whether to pin the memory. Defaults to False.
+            drop_last (bool, optional): Flag indicating whether to drop the last incomplete batch. Defaults to False.
+        """
         self.num_batches = len(dataset) // batch_size + int(not drop_last)
         dataset = dataset.batched(batch_size, collation_fn=collate_fn)
         self.webloader = wd.WebLoader(dataset=dataset, batch_size=None, num_workers=num_workers, pin_memory=pin_memory)

src/modalities/models/coca/coca_model.py

@@ -309,7 +309,10 @@ def __init__(
         # Logit scale for contrastive loss
         self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
 
-    def _init_modality(self, encoder_class, encoder_config, n_queries):
+    def _init_modality(
+        self, encoder_class: type, encoder_config: VisionTransformerConfig | AudioTransformerConfig, n_queries: int
+    ) -> tuple[VisionTransformer | AudioTransformer, nn.Parameter, AttentionPooling]:
+        # initialize modality encoder, returns a tuple containing the encoder, queries and attention pooling layer
         encoder = encoder_class(**dict(encoder_config))
         queries = nn.Parameter(torch.randn(n_queries + 1, encoder_config.n_embd))
         attn_pool = AttentionPooling(