Add docstrings for abstract preprocessing class

zookeeper/preprocessing.py

@@ -2,15 +2,24 @@
 
 
 class Preprocessing(abc.ABC):
-    """ An abstract Preprocessing class that contains the abstract contains the
-    methods required for Preprocessing.
-
-    We define a neural network to be a mapping between an input and an output.
-    hence we define to abstract methods, an input (e.g the image for image
-    classification), and an output (e.g the class label for image classification).
-    We also define decoders, which allows use to customize the decoding, and kwargs,
-    which allows one to pass information from the preprocessing to the model (e.g
-    input size.)
+    """An abstract class to be used to define data preprocessing.
+
+    We define a neural network to be a mapping between an input and an output, hence we
+    define to abstract methods, an input (e.g the image for image classification), and
+    an output (e.g the class label for image classification). We also define decoders,
+    which allows use to customize the decoding, and kwargs, which allows one to pass
+    information from the preprocessing to the model (e.g input size.)
+
+    # Arguments
+    features: A [`tfds.features.FeaturesDict`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeaturesDict)
+
+    # Properties
+    - `decoders`: Nested `dict` of [`Decoder`](https://www.tensorflow.org/datasets/api_docs/python/tfds/decode/Decoder)
+        objects which allow to customize the decoding. The structure should match the
+        feature structure, but only customized feature keys need to be present.
+        See [the guide](https://www.tensorflow.org/datasets/decode) for more info.
+    - `kwargs`: A `dict` that can be used to pass additional keyword arguments to a
+        model function.
     """
 
     decoders = None
@@ -21,8 +30,24 @@ def __init__(self, features=None):
 
     @abc.abstractmethod
     def inputs(self, data, training):
+        """A method to define preprocessing for inputs.
+
+        This method needs to be overwritten by all subclasses.
+
+        # Arguments
+        data: A dictionary of type {feature_name: Tensor}
+        training: A optional `boolean` to define if preprocessing is called during training.
+        """
         raise NotImplementedError("Must be implemented in subclasses.")
 
     @abc.abstractmethod
     def outputs(self, data, training):
+        """A method to define preprocessing for outputs.
+
+        This method needs to be overwritten by all subclasses.
+
+        # Arguments
+        data: A dictionary of type {feature_name: Tensor}
+        training: A optional `boolean` to define if preprocessing is called during training.
+        """
         raise NotImplementedError("Must be implemented in subclasses.")