diff --git a/monai/metrics/confusion_matrix.py b/monai/metrics/confusion_matrix.py
index bc06fe5855..a0c840d45a 100644
--- a/monai/metrics/confusion_matrix.py
+++ b/monai/metrics/confusion_matrix.py
@@ -87,7 +87,7 @@ def __call__(self, y_pred: torch.Tensor, y: torch.Tensor):
         dims = y_pred.ndimension()
         if dims < 2:
             raise ValueError("y_pred should have at least two dimensions.")
-        elif dims == 2 or (dims == 3 and y_pred.shape[-1] == 1):
+        if dims == 2 or (dims == 3 and y_pred.shape[-1] == 1):
             if self.compute_sample:
                 warnings.warn("As for classification task, compute_sample should be False.")
                 self.compute_sample = False
diff --git a/monai/networks/blocks/dynunet_block.py b/monai/networks/blocks/dynunet_block.py
index 03413ed816..d43958de0c 100644
--- a/monai/networks/blocks/dynunet_block.py
+++ b/monai/networks/blocks/dynunet_block.py
@@ -228,13 +228,12 @@ def get_acti_layer(act: Union[Tuple[str, Dict], str]):
 def get_norm_layer(spatial_dims: int, out_channels: int, norm_name: str, num_groups: int = 16):
     if norm_name not in ["batch", "instance", "group"]:
         raise ValueError(f"Unsupported normalization mode: {norm_name}")
+    if norm_name == "group":
+        assert out_channels % num_groups == 0, "out_channels should be divisible by num_groups."
+        norm = Norm[norm_name](num_groups=num_groups, num_channels=out_channels, affine=True)
     else:
-        if norm_name == "group":
-            assert out_channels % num_groups == 0, "out_channels should be divisible by num_groups."
-            norm = Norm[norm_name](num_groups=num_groups, num_channels=out_channels, affine=True)
-        else:
-            norm = Norm[norm_name, spatial_dims](out_channels, affine=True)
-        return norm
+        norm = Norm[norm_name, spatial_dims](out_channels, affine=True)
+    return norm
 
 
 def get_conv_layer(
diff --git a/monai/networks/blocks/segresnet_block.py b/monai/networks/blocks/segresnet_block.py
index 29a8a8e3aa..6aaa9774b5 100644
--- a/monai/networks/blocks/segresnet_block.py
+++ b/monai/networks/blocks/segresnet_block.py
@@ -22,16 +22,15 @@
 def get_norm_layer(spatial_dims: int, in_channels: int, norm_name: str, num_groups: int = 8):
     if norm_name not in ["batch", "instance", "group"]:
         raise ValueError(f"Unsupported normalization mode: {norm_name}")
+    if norm_name == "group":
+        norm = Norm[norm_name](num_groups=num_groups, num_channels=in_channels)
     else:
-        if norm_name == "group":
-            norm = Norm[norm_name](num_groups=num_groups, num_channels=in_channels)
-        else:
-            norm = Norm[norm_name, spatial_dims](in_channels)
-        if norm.bias is not None:
-            nn.init.zeros_(norm.bias)
-        if norm.weight is not None:
-            nn.init.ones_(norm.weight)
-        return norm
+        norm = Norm[norm_name, spatial_dims](in_channels)
+    if norm.bias is not None:
+        nn.init.zeros_(norm.bias)
+    if norm.weight is not None:
+        nn.init.ones_(norm.weight)
+    return norm
 
 
 def get_conv_layer(
diff --git a/monai/networks/layers/simplelayers.py b/monai/networks/layers/simplelayers.py
index ba60f4eca4..a6524669b1 100644
--- a/monai/networks/layers/simplelayers.py
+++ b/monai/networks/layers/simplelayers.py
@@ -233,8 +233,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = torch.as_tensor(x, device=x.device if torch.is_tensor(x) else None)
         if torch.is_complex(x):
             raise ValueError("x must be real.")
-        else:
-            x = x.to(dtype=torch.float)
+        x = x.to(dtype=torch.float)
 
         if (self.axis < 0) or (self.axis > len(x.shape) - 1):
             raise ValueError("Invalid axis for shape of x.")
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
index 6f94660ccd..ffbd91690c 100644
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -451,7 +451,7 @@ def create_rotate(spatial_dims: int, radians: Union[Sequence[float], float]) ->
             return np.array([[cos_, -sin_, 0.0], [sin_, cos_, 0.0], [0.0, 0.0, 1.0]])
         raise ValueError("radians must be non empty.")
 
-    elif spatial_dims == 3:
+    if spatial_dims == 3:
         affine = None
         if len(radians) >= 1:
             sin_, cos_ = np.sin(radians[0]), np.cos(radians[0])
diff --git a/monai/utils/aliases.py b/monai/utils/aliases.py
index 5a182862d5..ce3c513473 100644
--- a/monai/utils/aliases.py
+++ b/monai/utils/aliases.py
@@ -91,8 +91,7 @@ def resolve_name(name):
                     modnames = [m.__name__ for m in foundmods]
                     msg = f"Multiple modules ({modnames!r}) with declaration name {name!r} found, resolution is ambiguous."
                     raise ValueError(msg)
-                else:
-                    mods = list(foundmods)
+                mods = list(foundmods)
 
             obj = getattr(mods[0], name)
 
diff --git a/tests/utils.py b/tests/utils.py
index 157eef8aff..efedcdc859 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -407,8 +407,7 @@ def _wrapper(*args, **kwargs):
             if isinstance(res, Exception):  # other errors from obj
                 if hasattr(res, "traceback"):
                     raise RuntimeError(res.traceback) from res
-                else:
-                    raise res
+                raise res
             if timeout_error:  # no force_quit finished
                 raise timeout_error
             return res