updated formatting of changed sections

Author: Mithrillion

tensorly/tests/test_tucker_tensor.py

@@ -108,17 +108,23 @@ def test_tucker_to_tensor_with_partial_modes():
         for (R, s) in zip(ranks, tl.shape(X)[1:])
     ]
     true_res = np.array(
-        [[[ 120.,  456.,  792., 1128.],
-        [ 400., 1472., 2544., 3616.],
-        [ 680., 2488., 4296., 6104.]],
-
-       [[ 126.,  486.,  846., 1206.],
-        [ 422., 1582., 2742., 3902.],
-        [ 718., 2678., 4638., 6598.]],
-
-       [[ 132.,  516.,  900., 1284.],
-        [ 444., 1692., 2940., 4188.],
-        [ 756., 2868., 4980., 7092.]]]
+        [
+            [
+                [120.0, 456.0, 792.0, 1128.0],
+                [400.0, 1472.0, 2544.0, 3616.0],
+                [680.0, 2488.0, 4296.0, 6104.0],
+            ],
+            [
+                [126.0, 486.0, 846.0, 1206.0],
+                [422.0, 1582.0, 2742.0, 3902.0],
+                [718.0, 2678.0, 4638.0, 6598.0],
+            ],
+            [
+                [132.0, 516.0, 900.0, 1284.0],
+                [444.0, 1692.0, 2940.0, 4188.0],
+                [756.0, 2868.0, 4980.0, 7092.0],
+            ],
+        ]
     )
     res = tucker_to_tensor((X, U), modes=[1, 2])
     assert_array_equal(true_res, res)

tensorly/tucker_tensor.py

@@ -47,7 +47,9 @@ def _validate_tucker_tensor(tucker_tensor):
     return tuple(shape), tuple(rank)
 
 
-def tucker_to_tensor(tucker_tensor, skip_factor=None, transpose_factors=False, modes=None):
+def tucker_to_tensor(
+    tucker_tensor, skip_factor=None, transpose_factors=False, modes=None
+):
     """Converts the Tucker tensor into a full tensor
 
     Parameters
@@ -68,7 +70,9 @@ def tucker_to_tensor(tucker_tensor, skip_factor=None, transpose_factors=False, m
        full tensor of shape ``(factors[0].shape[0], ..., factors[-1].shape[0])``
     """
     core, factors = tucker_tensor
-    return multi_mode_dot(core, factors, skip=skip_factor, transpose=transpose_factors, modes=modes)
+    return multi_mode_dot(
+        core, factors, skip=skip_factor, transpose=transpose_factors, modes=modes
+    )
 
 
 def tucker_normalize(tucker_tensor):