Assert dimension consistency even without label weights

PiperOrigin-RevId: 762307281

Author: tensorflower-gardener

tf_keras/metrics/confusion_metrics.py

@@ -1471,9 +1471,10 @@ def update_state(self, y_true, y_pred, sample_weight=None):
                 # label_weights should be of length equal to the number of
                 # labels.
                 shapes.append((self.label_weights, ("L",)))
-                tf.debugging.assert_shapes(
-                    shapes, message="Number of labels is not consistent."
-                )
+
+            tf.debugging.assert_shapes(
+                shapes, message="Number of labels is not consistent."
+            )
 
         # Only forward label_weights to update_confusion_matrix_variables when
         # multi_label is False. Otherwise the averaging of individual label AUCs