(doc/fluxion/linear) add/convert docstrings to mkdocstrings format

src/refiners/fluxion/layers/linear.py

@@ -1,5 +1,4 @@
-from jaxtyping import Float
-from torch import Tensor, device as Device, dtype as DType
+from torch import device as Device, dtype as DType
 from torch.nn import Linear as _Linear
 
 from refiners.fluxion.layers.activations import ReLU
@@ -8,6 +7,27 @@ from refiners.fluxion.layers.module import Module, WeightedModule
 
 
 class Linear(_Linear, WeightedModule):
+    """Linear layer.
+
+    This layer wraps [`torch.nn.Linear`][torch.nn.Linear].
+
+    Receives:
+        Input (Float[Tensor, "batch in_features"]):
+
+    Returns:
+        Output (Float[Tensor, "batch out_features"]):
+
+    Example:
+        ```py
+        linear = fl.Linear(in_features=32, out_features=128)
+
+        tensor = torch.randn(2, 32)
+        output = linear(tensor)
+
+        assert output.shape == (2, 128)
+        ```
+    """
+
     def __init__(
         self,
         in_features: int,
@@ -16,6 +36,15 @@ class Linear(_Linear, WeightedModule):
         device: Device | str | None = None,
         dtype: DType | None = None,
     ) -> None:
+        """Initializes the Linear layer.
+
+        Args:
+            in_features: The number of input features.
+            out_features: The number of output features.
+            bias: If True, adds a learnable bias to the output.
+            device: The device to use for the linear layer.
+            dtype: The dtype to use for the linear layer.
+        """
         self.in_features = in_features
         self.out_features = out_features
         super().__init__(  # type: ignore
@@ -26,11 +55,35 @@ class Linear(_Linear, WeightedModule):
             dtype=dtype,
         )
 
-    def forward(self, x: Float[Tensor, "batch in_features"]) -> Float[Tensor, "batch out_features"]:  # type: ignore
-        return super().forward(x)
-
 
 class MultiLinear(Chain):
+    """Multi-layer linear network.
+
+    This layer wraps multiple [`torch.nn.Linear`][torch.nn.Linear] layers,
+    with an [`Activation`][refiners.fluxion.layers.Activation] layer in between.
+
+    Receives:
+        Input (Float[Tensor, "batch input_dim"]):
+
+    Returns:
+        Output (Float[Tensor, "batch output_dim"]):
+
+    Example:
+        ```py
+        linear = fl.MultiLinear(
+            input_dim=32,
+            output_dim=128,
+            inner_dim=64,
+            num_layers=3,
+        )
+
+        tensor = torch.randn(2, 32)
+        output = linear(tensor)
+
+        assert output.shape == (2, 128)
+        ```
+    """
+
     def __init__(
         self,
         input_dim: int,
@@ -40,10 +93,36 @@ class MultiLinear(Chain):
         device: Device | str | None = None,
         dtype: DType | None = None,
     ) -> None:
+        """Initializes the MultiLinear layer.
+
+        Args:
+            input_dim: The input dimension of the first linear layer.
+            output_dim: The output dimension of the last linear layer.
+            inner_dim: The output dimension of the inner linear layers.
+            num_layers: The number of linear layers.
+            device: The device to use for the linear layers.
+            dtype: The dtype to use for the linear layers.
+        """
         layers: list[Module] = []
         for i in range(num_layers - 1):
-            layers.append(Linear(input_dim if i == 0 else inner_dim, inner_dim, device=device, dtype=dtype))
-            layers.append(ReLU())
-        layers.append(Linear(inner_dim, output_dim, device=device, dtype=dtype))
+            layers.append(
+                Linear(
+                    in_features=input_dim if i == 0 else inner_dim,
+                    out_features=inner_dim,
+                    device=device,
+                    dtype=dtype,
+                )
+            )
+            layers.append(
+                ReLU(),
+            )
+        layers.append(
+            Linear(
+                in_features=inner_dim,
+                out_features=output_dim,
+                device=device,
+                dtype=dtype,
+            )
+        )
 
         super().__init__(layers)