Remove the Defaults to code doc pattern (#45)

src/eva/data/datamodules/datamodule.py

@@ -27,8 +27,8 @@ def __init__(
         """Initializes the datamodule.
 
         Args:
-            datasets: The desired datasets. Defaults to `None`.
-            dataloaders: The desired dataloaders. Defaults to `None`.
+            datasets: The desired datasets.
+            dataloaders: The desired dataloaders.
         """
         super().__init__()
 

src/eva/metrics/core/module.py

@@ -53,7 +53,7 @@ def from_metrics(
             val: Metrics for the validation stage.
             test: Metrics for the test stage.
             separator: The separator between the group name of the metric
-                and the metric itself. Defaults to `"/"`.
+                and the metric itself.
         """
         return cls(
             train=_create_collection_from_metrics(train, prefix="train" + separator),
@@ -73,7 +73,7 @@ def from_schema(
         Args:
             schema: The dataclass metric schema.
             separator: The separator between the group name of the metric
-                and the metric itself. Defaults to `"/"`.
+                and the metric itself.
         """
         return cls.from_metrics(
             train=schema.training_metrics,
@@ -105,7 +105,7 @@ def _create_collection_from_metrics(
 
     Args:
         metrics: The desired metrics.
-        prefix: A prefix to added to the collection. Defaults to `None`.
+        prefix: A prefix to added to the collection.
 
     Returns:
         The resulted metrics collection.

src/eva/models/modules/head.py

@@ -40,13 +40,10 @@ def __init__(
             criterion: The loss function to use.
             backbone: The feature extractor. If `None`, it will be expected
                 that the input batch returns the features directly.
-                Defaults to `None`.
             optimizer: The optimizer to use.
-                Defaults to :class:`torch.optim.Adam`.
             lr_scheduler: The learning rate scheduler to use.
-                Defaults to :class:`torch.optim.lr_scheduler.ConstantLR`.
             metrics: The list of metrics to track. If `None`, it uses
-                the :meth:`self.default_metrics`. Defaults to `None`.
+                the :meth:`self.default_metrics`.
         """
         super().__init__(metrics=metrics)
 

src/eva/models/modules/module.py

@@ -23,7 +23,7 @@ def __init__(
         """Initializes the basic module.
 
         Args:
-            metrics: The metrics schema. Defaults to `None`.
+            metrics: The metrics schema.
         """
         super().__init__()
 
@@ -131,7 +131,7 @@ def _update_metrics(
         Args:
             metrics: The desired metrics tracker to update.
             batch_outputs: The outputs of the batch processing step.
-            dataloader_idx: The dataloader index. Defaults to `0`.
+            dataloader_idx: The dataloader index.
         """
         inputs = self._parse_metrics_inputs(batch_outputs, dataloader_idx)
         metrics.update(**inputs)
@@ -159,7 +159,7 @@ def _parse_metrics_inputs(
 
         Args:
             batch_outputs: The outputs of the batch processing step.
-            dataloader_idx: The dataloader index. Defaults to `0`.
+            dataloader_idx: The dataloader index.
 
         Returns:
             A mapping with the argument name and its value.