diff --git a/nilearn/_utils/logger.py b/nilearn/_utils/logger.py
index 9a02e2e1da..5a5a209a71 100644
--- a/nilearn/_utils/logger.py
+++ b/nilearn/_utils/logger.py
@@ -71,36 +71,37 @@ def log(
     is the one which is most likely to have been written in the user's script.
 
     """
-    if verbose >= msg_level:
-        stack = inspect.stack()
-        object_frame = None
-        object_self = None
-        for f in reversed(stack):
-            frame = f[0]
-            current_self = frame.f_locals.get("self", None)
-            if isinstance(current_self, object_classes):
-                object_frame = frame
-                func_name = f[3]
-                object_self = current_self
-                break
-
-        if object_frame is None:  # no object found: use stack_level
-            if stack_level >= len(stack):
-                func_name = "<top_level>"
-            else:
-                object_frame, _, _, func_name = stack[stack_level][:4]
-                object_self = object_frame.f_locals.get("self", None)
-
-        if object_self is not None:
-            func_name = f"{object_self.__class__.__name__}.{func_name}"
-
-        if _has_rich():
-            print(f"[blue]\\[{func_name}][/blue] {escape(msg)}")
+    if verbose < msg_level:
+        return
+    stack = inspect.stack()
+    object_frame = None
+    object_self = None
+    for f in reversed(stack):
+        frame = f[0]
+        current_self = frame.f_locals.get("self", None)
+        if isinstance(current_self, object_classes):
+            object_frame = frame
+            func_name = f[3]
+            object_self = current_self
+            break
+
+    if object_frame is None:  # no object found: use stack_level
+        if stack_level >= len(stack):
+            func_name = "<top_level>"
         else:
-            print(f"[{func_name}] {msg}")
+            object_frame, _, _, func_name = stack[stack_level][:4]
+            object_self = object_frame.f_locals.get("self", None)
 
-        if with_traceback:
-            traceback.print_exc()
+    if object_self is not None:
+        func_name = f"{object_self.__class__.__name__}.{func_name}"
+
+    if _has_rich():
+        print(f"[blue]\\[{func_name}][/blue] {escape(msg)}")
+    else:
+        print(f"[{func_name}] {msg}")
+
+    if with_traceback:
+        traceback.print_exc()
 
 
 def compose_err_msg(msg, **kwargs):
diff --git a/nilearn/_utils/numpy_conversions.py b/nilearn/_utils/numpy_conversions.py
index af91e91b7f..01060a4b66 100644
--- a/nilearn/_utils/numpy_conversions.py
+++ b/nilearn/_utils/numpy_conversions.py
@@ -73,7 +73,7 @@ def as_ndarray(arr, copy=False, dtype=None, order="K"):
     if isinstance(arr, (np.memmap, list, tuple)) or (
         isinstance(arr, np.ndarray) and copy
     ):
-        ret = np.array(arr, copy=True, dtype=dtype, order=order)
+        return np.array(arr, copy=True, dtype=dtype, order=order)
     # if the order does not change and dtype does not change or
     # bool to/from 1-byte dtype,
     # no need to create a copy
@@ -86,10 +86,9 @@ def as_ndarray(arr, copy=False, dtype=None, order="K"):
         or (order == "C" and arr.flags["C_CONTIGUOUS"])
         or order in ("K", "A", None)
     ):
-        ret = arr.view(dtype=dtype)
+        return arr.view(dtype=dtype)
     else:
-        ret = np.asarray(arr, dtype=dtype, order=order)
-    return ret
+        return np.asarray(arr, dtype=dtype, order=order)
 
 
 def csv_to_array(csv_path, delimiters=" \t,;", **kwargs):
diff --git a/nilearn/_utils/param_validation.py b/nilearn/_utils/param_validation.py
index 59095805e5..8677e67e26 100644
--- a/nilearn/_utils/param_validation.py
+++ b/nilearn/_utils/param_validation.py
@@ -106,14 +106,13 @@ def _get_mask_extent(mask_img):
     if hasattr(mask_img, "affine"):
         affine = mask_img.affine
         prod_vox_dims = 1.0 * np.abs(np.linalg.det(affine[:3, :3]))
-        mask_extent = prod_vox_dims * _get_data(mask_img).astype(bool).sum()
+        return prod_vox_dims * _get_data(mask_img).astype(bool).sum()
     else:
         # sum number of True values in both hemispheres
-        mask_extent = (
+        return (
             mask_img.data.parts["left"].sum()
             + mask_img.data.parts["right"].sum()
         )
-    return mask_extent
 
 
 def adjust_screening_percentile(