diff --git a/pytorch3d/implicitron/models/implicit_function/decoding_functions.py b/pytorch3d/implicitron/models/implicit_function/decoding_functions.py
index fb7494a7..eef3481a 100644
--- a/pytorch3d/implicitron/models/implicit_function/decoding_functions.py
+++ b/pytorch3d/implicitron/models/implicit_function/decoding_functions.py
@@ -43,27 +43,8 @@ class DecoderFunctionBase(ReplaceableBase, torch.nn.Module):
     """
     Decoding function is a torch.nn.Module which takes the embedding of a location in
     space and transforms it into the required quantity (for example density and color).
-
-    Members:
-        param_groups: dictionary where keys are names of individual parameters
-            or module members and values are the parameter group where the
-            parameter/member will be sorted to. "self" key is used to denote the
-            parameter group at the module level. Possible keys, including the "self" key
-            do not have to be defined. By default all parameters are put into "default"
-            parameter group and have the learning rate defined in the optimizer,
-            it can be overridden at the:
-                - module level with “self” key, all the parameters and child
-                    module's parameters will be put to that parameter group
-                - member level, which is the same as if the `param_groups` in that
-                    member has key=“self” and value equal to that parameter group.
-                    This is useful if members do not have `param_groups`, for
-                    example torch.nn.Linear.
-                - parameter level, parameter with the same name as the key
-                    will be put to that parameter group.
     """
 
-    param_groups: Dict[str, str] = field(default_factory=lambda: {})
-
     def __post_init__(self):
         super().__init__()
 
@@ -280,11 +261,30 @@ class MLPWithInputSkips(Configurable, torch.nn.Module):
 class MLPDecoder(DecoderFunctionBase):
     """
     Decoding function which uses `MLPWithIputSkips` to convert the embedding to output.
-    If using Implicitron config system `input_dim` of the `network` is changed to the
-    value of `input_dim` member and `input_skips` is removed.
+    The `input_dim` of the `network` is set from the value of `input_dim` member.
+
+    Members:
+        input_dim: dimension of input.
+        param_groups: dictionary where keys are names of individual parameters
+            or module members and values are the parameter group where the
+            parameter/member will be sorted to. "self" key is used to denote the
+            parameter group at the module level. Possible keys, including the "self" key
+            do not have to be defined. By default all parameters are put into "default"
+            parameter group and have the learning rate defined in the optimizer,
+            it can be overridden at the:
+                - module level with “self” key, all the parameters and child
+                    module's parameters will be put to that parameter group
+                - member level, which is the same as if the `param_groups` in that
+                    member has key=“self” and value equal to that parameter group.
+                    This is useful if members do not have `param_groups`, for
+                    example torch.nn.Linear.
+                - parameter level, parameter with the same name as the key
+                    will be put to that parameter group.
+        network_args: configuration for MLPWithInputSkips
     """
 
     input_dim: int = 3
+    param_groups: Dict[str, str] = field(default_factory=lambda: {})
     network: MLPWithInputSkips
 
     def __post_init__(self):