test_var.py has been modified in order to respect the flake8 style.

theano/sandbox/cuda/tests/test_var.py

@@ -10,7 +10,7 @@ from theano.sandbox.cuda.var import float32_shared_constructor as f32sc
 from theano.sandbox.cuda import CudaNdarrayType, cuda_available
 import theano.sandbox.cuda as cuda
 # Skip test if cuda_ndarray is not available.
-if cuda_available == False:
+if cuda_available is False:
     raise SkipTest('Optional package cuda disabled')
 
 
@@ -26,20 +26,19 @@ def test_float32_shared_constructor():
 
     # test that broadcastable arg is accepted, and that they
     # don't strictly have to be tuples
-    assert eq(
-            f32sc(npy_row, broadcastable=(True, False)).type,
-            CudaNdarrayType((True, False)))
-    assert eq(
-            f32sc(npy_row, broadcastable=[True, False]).type,
-            CudaNdarrayType((True, False)))
-    assert eq(
-            f32sc(npy_row, broadcastable=numpy.array([True, False])).type,
-            CudaNdarrayType([True, False]))
+    assert eq(f32sc(npy_row,
+                    broadcastable=(True, False)).type,
+              CudaNdarrayType((True, False)))
+    assert eq(f32sc(npy_row,
+                    broadcastable=[True, False]).type,
+              CudaNdarrayType((True, False)))
+    assert eq(f32sc(npy_row,
+                    broadcastable=numpy.array([True, False])).type,
+              CudaNdarrayType([True, False]))
 
     # test that we can make non-matrix shared vars
-    assert eq(
-            f32sc(numpy.zeros((2, 3, 4, 5), dtype='float32')).type,
-            CudaNdarrayType((False,) * 4))
+    assert eq(f32sc(numpy.zeros((2, 3, 4, 5), dtype='float32')).type,
+              CudaNdarrayType((False,) * 4))
 
 
 def test_givens():
@@ -72,13 +71,14 @@ class T_updates(unittest.TestCase):
         # This test case uses code mentionned in #698
         data = numpy.random.rand(10, 10).astype('float32')
         output_var = f32sc(name="output",
-                value=numpy.zeros((10, 10), 'float32'))
+                           value=numpy.zeros((10, 10), 'float32'))
 
         x = tensor.fmatrix('x')
         output_updates = [(output_var, x ** 2)]
         output_givens = {x: data}
-        output_func = theano.function(inputs=[], outputs=[],
-                updates=output_updates, givens=output_givens)
+        output_func = theano.function(
+            inputs=[], outputs=[],
+            updates=output_updates, givens=output_givens)
         output_func()
 
     def test_err_ndim(self):