Use L2SqrtExpanded in pairwise_distances

Previously we were using `L2SqrtUnexpanded`, which is more costly.

python/cuml/cuml/metrics/pairwise_distances.pyx

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -66,9 +66,9 @@ cdef extern from "cuml/metrics/metrics.hpp" namespace "ML::Metrics":
 PAIRWISE_DISTANCE_METRICS = {
     "cityblock": DistanceType.L1,
     "cosine": DistanceType.CosineExpanded,
-    "euclidean": DistanceType.L2SqrtUnexpanded,
+    "euclidean": DistanceType.L2SqrtExpanded,
     "l1": DistanceType.L1,
-    "l2": DistanceType.L2SqrtUnexpanded,
+    "l2": DistanceType.L2SqrtExpanded,
     "manhattan": DistanceType.L1,
     "sqeuclidean": DistanceType.L2Expanded,
     "canberra": DistanceType.Canberra,
@@ -102,20 +102,6 @@ PAIRWISE_DISTANCE_SPARSE_METRICS = {
 
 
 def _determine_metric(metric_str, is_sparse_=False):
-    # Available options in scikit-learn and their pairs. See
-    # sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS:
-    # 'cityblock': L1
-    # 'cosine': CosineExpanded
-    # 'euclidean': L2SqrtUnexpanded
-    # 'haversine': N/A
-    # 'l2': L2SqrtUnexpanded
-    # 'l1': L1
-    # 'manhattan': L1
-    # 'nan_euclidean': N/A
-    # 'sqeuclidean': L2Unexpanded
-    # Note: many are duplicates following this:
-    # https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/pairwise.py#L1321
-
     if metric_str == 'haversine':
         raise ValueError(" The metric: '{}', is not supported at this time."
                          .format(metric_str))