message: Reduce the probability of failure of test_dtm
  It is expected that hnsw sometimes misses one neighbor, which has an
  impact on the DTM, especially if the number of neighbors considered is
  low.
diff: @@ -16,7 +16,7 @@ import torch
  def test_dtm_compare_euclidean():
  pts = numpy.random.rand(1000, 4)
  - k = 3
  + k = 6
  dtm = DistanceToMeasure(k, implementation="ckdtree")
  r0 = dtm.fit_transform(pts)
  dtm = DistanceToMeasure(k, implementation="sklearn")
  @@ -27,7 +27,7 @@ def test_dtm_compare_euclidean():
  assert r2 == pytest.approx(r0)
  dtm = DistanceToMeasure(k, implementation="hnsw")
  r3 = dtm.fit_transform(pts)
  - assert r3 == pytest.approx(r0)
  + assert r3 == pytest.approx(r0, rel=0.1)
  from scipy.spatial.distance import cdist
  d = cdist(pts, pts)
  