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

src/refiners/fluxion/layers/sampling.py

@@ -10,7 +10,32 @@ from refiners.fluxion.layers.module import Module
 from refiners.fluxion.utils import interpolate
 
 
+class Interpolate(Module):
+    """Interpolate layer.
+
+    This layer wraps [`torch.nn.functional.interpolate`][torch.nn.functional.interpolate].
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(
+        self,
+        x: Tensor,
+        shape: Size,
+    ) -> Tensor:
+        return interpolate(x, shape)
+
+
 class Downsample(Chain):
+    """Downsample layer.
+
+    This layer downsamples the input by the given scale factor.
+
+    Raises:
+        RuntimeError: If the context sampling is not set or if the context does not contain a list.
+    """
+
     def __init__(
         self,
         channels: int,
@@ -20,16 +45,22 @@ class Downsample(Chain):
         device: Device | str | None = None,
         dtype: DType | None = None,
     ):
-        """Downsamples the input by the given scale factor.
+        """Initializes the Downsample layer.
 
-        If register_shape is True, the input shape is registered in the context. It will throw an error if the context
+        Args:
-        sampling is not set or if the context does not contain a list.
+            channels: The number of input and output channels.
+            scale_factor: The factor by which to downsample the input.
+            padding: The amount of zero-padding added to both sides of the input.
+            register_shape: If True, registers the input shape in the context.
+            device: The device to use for the convolutional layer.
+            dtype: The dtype to use for the convolutional layer.
         """
         self.channels = channels
         self.in_channels = channels
         self.out_channels = channels
         self.scale_factor = scale_factor
         self.padding = padding
+
         super().__init__(
             Conv2d(
                 in_channels=channels,
@@ -41,25 +72,41 @@ class Downsample(Chain):
                 dtype=dtype,
             ),
         )
+
         if padding == 0:
             zero_pad: Callable[[Tensor], Tensor] = lambda x: pad(x, (0, 1, 0, 1))
-            self.insert(0, Lambda(zero_pad))
+            self.insert(
-        if register_shape:
+                index=0,
-            self.insert(0, SetContext(context="sampling", key="shapes", callback=self.register_shape))
+                module=Lambda(func=zero_pad),
+            )
 
-    def register_shape(self, shapes: list[Size], x: Tensor) -> None:
+        if register_shape:
+            self.insert(
+                index=0,
+                module=SetContext(
+                    context="sampling",
+                    key="shapes",
+                    callback=self.register_shape,
+                ),
+            )
+
+    def register_shape(
+        self,
+        shapes: list[Size],
+        x: Tensor,
+    ) -> None:
         shapes.append(x.shape[2:])
 
 
-class Interpolate(Module):
-    def __init__(self) -> None:
-        super().__init__()
-
-    def forward(self, x: Tensor, shape: Size) -> Tensor:
-        return interpolate(x, shape)
-
-
 class Upsample(Chain):
+    """Upsample layer.
+
+    This layer upsamples the input by the given scale factor.
+
+    Raises:
+        RuntimeError: If the context sampling is not set or if the context is empty.
+    """
+
     def __init__(
         self,
         channels: int,
@@ -67,10 +114,14 @@ class Upsample(Chain):
         device: Device | str | None = None,
         dtype: DType | None = None,
     ):
-        """Upsamples the input by the given scale factor.
+        """Initializes the Upsample layer.
 
-        If upsample_factor is None, the input shape is taken from the context. It will throw an error if the context
+        Args:
-        sampling is not set or if the context is empty (then you should use the dynamic version of Downsample).
+            channels: The number of input and output channels.
+            upsample_factor: The factor by which to upsample the input.
+                If None, the input shape is taken from the context.
+            device: The device to use for the convolutional layer.
+            dtype: The dtype to use for the convolutional layer.
         """
         self.channels = channels
         self.upsample_factor = upsample_factor