Minor flake8 fix

Author: mvargas33

test/test_bilinear_mixin.py

@@ -4,9 +4,9 @@
 from numpy.testing import assert_array_almost_equal
 import pytest
 from metric_learn._util import make_context
-from sklearn import clone
 from sklearn.cluster import DBSCAN
 
+
 class IdentityBilinearMixin(BilinearMixin):
   """A simple Identity bilinear mixin that returns an identity matrix
   M as learned. Can change M for a random matrix calling random_M.
@@ -55,6 +55,7 @@ def test_check_correctness_similarity():
   assert_array_almost_equal(dist1, desired)  # score_pairs
   assert_array_almost_equal(dist2, desired)  # get_metric
 
+
 def test_check_handmade_example():
   u = np.array([0, 1, 2])
   v = np.array([3, 4, 5])
@@ -104,8 +105,8 @@ def test_score_pairs_dim():
   assert mixin.score_pairs(tuples).shape == (tuples.shape[0],)
   context = make_context(mixin)
   msg = ("3D array of formed tuples expected{}. Found 2D array "
-      "instead:\ninput={}. Reshape your data and/or use a preprocessor.\n"
-      .format(context, tuples[1]))
+         "instead:\ninput={}. Reshape your data and/or use a preprocessor.\n"
+         .format(context, tuples[1]))
   with pytest.raises(ValueError) as raised_error:
     mixin.score_pairs(tuples[1])
   assert str(raised_error.value) == msg
@@ -122,4 +123,4 @@ def test_check_scikitlearn_compatibility():
   n = 100
   X = np.array([np.random.rand(d) for i in range(n)])
   clustering = DBSCAN(metric=mixin.get_metric())
-  clustering.fit(X)
+  clustering.fit(X)