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

src/refiners/fluxion/layers/norm.py

@@ -1,10 +1,37 @@
 from jaxtyping import Float
-from torch import Tensor, device as Device, dtype as DType, nn, ones, sqrt, zeros
+from torch import Tensor, device as Device, dtype as DType, ones, sqrt, zeros
+from torch.nn import (
+    GroupNorm as _GroupNorm,
+    InstanceNorm2d as _InstanceNorm2d,
+    LayerNorm as _LayerNorm,
+    Parameter as TorchParameter,
+)
 
 from refiners.fluxion.layers.module import Module, WeightedModule
 
 
-class LayerNorm(nn.LayerNorm, WeightedModule):
+class LayerNorm(_LayerNorm, WeightedModule):
+    """Layer Normalization layer.
+
+    This layer wraps [`torch.nn.LayerNorm`][torch.nn.LayerNorm].
+
+    Receives:
+        (Float[Tensor, "batch *normalized_shape"]):
+
+    Returns:
+        (Float[Tensor, "batch *normalized_shape"]):
+
+    Example:
+        ```py
+        layernorm = fl.LayerNorm(normalized_shape=128)
+
+        tensor = torch.randn(2, 128)
+        output = layernorm(tensor)
+
+        assert output.shape == (2, 128)
+        ```
+    """
+
     def __init__(
         self,
         normalized_shape: int | list[int],
@@ -21,7 +48,28 @@ class LayerNorm(nn.LayerNorm, WeightedModule):
         )
 
 
-class GroupNorm(nn.GroupNorm, WeightedModule):
+class GroupNorm(_GroupNorm, WeightedModule):
+    """Group Normalization layer.
+
+    This layer wraps [`torch.nn.GroupNorm`][torch.nn.GroupNorm].
+
+    Receives:
+        (Float[Tensor, "batch channels *normalized_shape"]):
+
+    Returns:
+        (Float[Tensor, "batch channels *normalized_shape"]):
+
+    Example:
+        ```py
+        groupnorm = fl.GroupNorm(channels=128, num_groups=8)
+
+        tensor = torch.randn(2, 128, 8)
+        output = groupnorm(tensor)
+
+        assert output.shape == (2, 128, 8)
+        ```
+    """
+
     def __init__(
         self,
         channels: int,
@@ -44,12 +92,15 @@ class GroupNorm(nn.GroupNorm, WeightedModule):
 
 
 class LayerNorm2d(WeightedModule):
-    """
+    """2D Layer Normalization layer.
-    2D Layer Normalization module.
 
-    Parameters:
+    This layer applies Layer Normalization along the 2nd dimension of a 4D tensor.
-        channels (int): Number of channels in the input tensor.
+
-        eps (float, optional): A small constant for numerical stability. Default: 1e-6.
+    Receives:
+        (Float[Tensor, "batch channels height width"]):
+
+    Returns:
+        (Float[Tensor, "batch channels height width"]):
     """
 
     def __init__(
@@ -60,11 +111,14 @@ class LayerNorm2d(WeightedModule):
         dtype: DType | None = None,
     ) -> None:
         super().__init__()
-        self.weight = nn.Parameter(ones(channels, device=device, dtype=dtype))
+        self.weight = TorchParameter(ones(channels, device=device, dtype=dtype))
-        self.bias = nn.Parameter(zeros(channels, device=device, dtype=dtype))
+        self.bias = TorchParameter(zeros(channels, device=device, dtype=dtype))
         self.eps = eps
 
-    def forward(self, x: Float[Tensor, "batch channels height width"]) -> Float[Tensor, "batch channels height width"]:
+    def forward(
+        self,
+        x: Float[Tensor, "batch channels height width"],
+    ) -> Float[Tensor, "batch channels height width"]:
         x_mean = x.mean(1, keepdim=True)
         x_var = (x - x_mean).pow(2).mean(1, keepdim=True)
         x_norm = (x - x_mean) / sqrt(x_var + self.eps)
@@ -72,7 +126,18 @@ class LayerNorm2d(WeightedModule):
         return x_out
 
 
-class InstanceNorm2d(nn.InstanceNorm2d, Module):
+class InstanceNorm2d(_InstanceNorm2d, Module):
+    """Instance Normalization layer.
+
+    This layer wraps [`torch.nn.InstanceNorm2d`][torch.nn.InstanceNorm2d].
+
+    Receives:
+        (Float[Tensor, "batch channels height width"]):
+
+    Returns:
+        (Float[Tensor, "batch channels height width"]):
+    """
+
     def __init__(
         self,
         num_features: int,