diff --git a/benchmarks/python/gather_bench.py b/benchmarks/python/gather_bench.py
index e000841d2..ae6fb8f5f 100644
--- a/benchmarks/python/gather_bench.py
+++ b/benchmarks/python/gather_bench.py
@@ -1,7 +1,6 @@
 # Copyright © 2023-2024 Apple Inc.
 
 import argparse
-from time import time
 
 import mlx.core as mx
 import torch
diff --git a/python/mlx/nn/layers/base.py b/python/mlx/nn/layers/base.py
index f141cfc0f..b35c58478 100644
--- a/python/mlx/nn/layers/base.py
+++ b/python/mlx/nn/layers/base.py
@@ -81,7 +81,7 @@ class Module(dict):
         """
         return self
 
-    def _extra_repr(self):
+    def _extra_repr(self) -> str:
         return ""
 
     def __repr__(self):
@@ -210,7 +210,7 @@ class Module(dict):
             mx.save_safetensors(file, params_dict)
         else:
             raise ValueError(
-                "Unsupported file extension. Use '.npz' or '.safetensors'."
+                f"Unsupported file extension for {file}. Use '.npz' or '.safetensors'."
             )
 
     @staticmethod
@@ -598,9 +598,7 @@ class Module(dict):
               parameters to the new dtype.
         """
         if predicate is None:
-
-            def predicate(_):
-                return True
+            predicate = lambda _: True
 
         self.apply(lambda x: x.astype(dtype) if predicate(x.dtype) else x)
 
diff --git a/python/mlx/nn/losses.py b/python/mlx/nn/losses.py
index ebf05d8ff..bccf45b16 100644
--- a/python/mlx/nn/losses.py
+++ b/python/mlx/nn/losses.py
@@ -1,7 +1,7 @@
 # Copyright © 2023 Apple Inc.
 
 import math
-from typing import Literal, Optional
+from typing import Literal, Optional, get_args
 
 import mlx.core as mx
 
@@ -9,14 +9,15 @@ Reduction = Literal["none", "mean", "sum"]
 
 
 def _reduce(loss: mx.array, reduction: Reduction = "none"):
+    if reduction not in get_args(Reduction):
+        raise ValueError(f"Invalid reduction. Must be one of {get_args(Reduction)}.")
+
     if reduction == "mean":
         return mx.mean(loss)
     elif reduction == "sum":
         return mx.sum(loss)
     elif reduction == "none":
         return loss
-    else:
-        raise ValueError("Invalid reduction. Must be 'none', 'mean', or 'sum'.")
 
 
 def cross_entropy(