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

src/refiners/fluxion/layers/conv.py

@@ -4,6 +4,33 @@ from refiners.fluxion.layers.module import WeightedModule
 
 
 class Conv2d(nn.Conv2d, WeightedModule):
+    """2D Convolutional layer.
+
+    This layer wraps [`torch.nn.Conv2d`][torch.nn.Conv2d].
+
+    Receives:
+        (Real[Tensor, "batch in_channels in_height in_width"]):
+
+    Returns:
+        (Real[Tensor, "batch out_channels out_height out_width"]):
+
+    Example:
+        ```py
+        conv2d = fl.Conv2d(
+            in_channels=3,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+        )
+
+        tensor = torch.randn(2, 3, 128, 128)
+        output = conv2d(tensor)
+
+        assert output.shape == (2, 32, 128, 128)
+        ```
+    """
+
     def __init__(
         self,
         in_channels: int,
@@ -19,22 +46,49 @@ class Conv2d(nn.Conv2d, WeightedModule):
         dtype: DType | None = None,
     ) -> None:
         super().__init__(  # type: ignore
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            use_bias,
-            padding_mode,
-            device,
-            dtype,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=use_bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
         )
         self.use_bias = use_bias
 
 
 class ConvTranspose2d(nn.ConvTranspose2d, WeightedModule):
+    """2D Transposed Convolutional layer.
+
+    This layer wraps [`torch.nn.ConvTranspose2d`][torch.nn.ConvTranspose2d].
+
+    Receives:
+        (Real[Tensor, "batch in_channels in_height in_width"]):
+
+    Returns:
+        (Real[Tensor, "batch out_channels out_height out_width"]):
+
+    Example:
+        ```py
+        conv2d = fl.ConvTranspose2d(
+            in_channels=3,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+        )
+
+        tensor = torch.randn(2, 3, 128, 128)
+        output = conv2d(tensor)
+
+        assert output.shape == (2, 32, 128, 128)
+        ```
+    """
+
     def __init__(
         self,
         in_channels: int,
@@ -64,3 +118,4 @@ class ConvTranspose2d(nn.ConvTranspose2d, WeightedModule):
             device=device,
             dtype=dtype,
         )
+        self.use_bias = use_bias