fix docs

pytorch_lightning/metrics/functional/classification.py

@@ -1,5 +1,6 @@
 from collections import Sequence
 from typing import Optional, Tuple, Callable
+from functools import wraps
 
 import torch
 
@@ -326,6 +327,7 @@ def auc(x: torch.Tensor, y: torch.Tensor, reorder: bool = True):
 
 def auc_decorator(reorder: bool = True) -> Callable:
     def wrapper(func_to_decorate: Callable) -> Callable:
+        @wraps(func_to_decorate)
         def new_func(*args, **kwargs) -> torch.Tensor:
             x, y = func_to_decorate(*args, **kwargs)[:2]
 

pytorch_lightning/metrics/functional/reduction.py

@@ -3,22 +3,16 @@
 
 def reduce(to_reduce: torch.Tensor, reduction: str) -> torch.Tensor:
     """
-    reduces a given tensor by a given reduction method
-    Parameters
-    ----------
-    to_reduce : torch.Tensor
-        the tensor, which shall be reduced
-    reduction : str
-        a string specifying the reduction method.
-        should be one of 'elementwise_mean' | 'none' | 'sum'
-    Returns
-    -------
-    torch.Tensor
+    Reduces a given tensor by a given reduction method
+
+    Args:
+        to_reduce : the tensor, which shall be reduced
+       reduction :  a string specifying the reduction method ('elementwise_mean', 'none', 'sum')
+
+    Returns:
         reduced Tensor
-    Raises
-    ------
-    ValueError
-        if an invalid reduction parameter was given
+
+    Raises: ValueError if an invalid reduction parameter was given
     """
     if reduction == 'elementwise_mean':
         return torch.mean(to_reduce)