fixing flake8 for test_blas.py

theano/tensor/tests/test_blas.py

@@ -875,7 +875,7 @@ def test_dot22scalar():
                     def check_dot22scalar(func, len_topo_scalar=-1):
                         topo = func.maker.fgraph.toposort()
                         ops = [x.op for x in topo]
-                        classes = [type(x.op) for x in topo]
+                        # classes = [type(x.op) for x in topo]
                         dtype4_upcast = theano.scalar.upcast(dtype4, dtype1,
                                                              dtype2)
 
@@ -914,7 +914,7 @@ def test_dot22scalar():
                         if False:
                             f = theano.function([a, b], cst * T.dot(a, b),
                                                 mode=mode_blas_opt)
-                            topo = f.maker.fgraph.toposort()
+                            f.maker.fgraph.toposort()
                             check_dot22scalar(f, 1)
 
                             f(av, bv)
@@ -923,7 +923,7 @@ def test_dot22scalar():
                             f = theano.function([a, b, c],
                                                 cst * c * T.dot(a, b),
                                                 mode=mode_blas_opt)
-                            topo = f.maker.fgraph.toposort()
+                            f.maker.fgraph.toposort()
                             check_dot22scalar(f, 2)
 
                             f(av, bv, cv)
@@ -931,7 +931,7 @@ def test_dot22scalar():
                         f = theano.function([a, b, c],
                                             c * cst * T.dot(a, b),
                                             mode=mode_blas_opt)
-                        topo = f.maker.fgraph.toposort()
+                        f.maker.fgraph.toposort()
                         check_dot22scalar(f, 2)
                         f(av, bv, cv)
 
@@ -941,7 +941,7 @@ def test_dot22scalar():
                         f = theano.function([a, b, c],
                                             cst2 * c * cst * T.dot(a, b),
                                             mode=m2)
-                        topo = f.maker.fgraph.toposort()
+                        f.maker.fgraph.toposort()
                         check_dot22scalar(f, 2)
                         f(av, bv, cv)
 
@@ -949,14 +949,14 @@ def test_dot22scalar():
                             f = theano.function([a, b, c],
                                                 c * cst * a * T.dot(a, b),
                                                 mode=m2)
-                            topo = f.maker.fgraph.toposort()
+                            f.maker.fgraph.toposort()
                             check_dot22scalar(f, 2)
                             f(sv, sv, sv)
 
                             f = theano.function([a, b, c],
                                                 cst * c * a * T.dot(a, b),
                                                 mode=mode_blas_opt)
-                            topo = f.maker.fgraph.toposort()
+                            f.maker.fgraph.toposort()
                             # currently the canonizer don't always
                             # merge all Mul together...  dot22scalar
                             # optimizer does not do a recursive search
@@ -972,7 +972,7 @@ def test_dot22scalar():
                             f = theano.function([a, b, c],
                                                 c * a * cst * T.dot(a, b),
                                                 mode=m2)
-                            topo = f.maker.fgraph.toposort()
+                            f.maker.fgraph.toposort()
                             check_dot22scalar(f, 2)
                             f(sv, sv, sv)
 
@@ -1330,7 +1330,7 @@ class BaseGemv(object):
 
         oy_func = theano.function([], oy, mode=self.mode)
 
-        topo = oy_func.maker.fgraph.toposort()
+        oy_func.maker.fgraph.toposort()
         self.assertFunctionContains1(oy_func, self.gemv)
 
         oy_val = oy_func()
@@ -1393,8 +1393,8 @@ class BaseGemv(object):
         alpha_v, beta_v, a_v, x_v, y_v = vs
         alpha, beta, a, x, y = [self.shared(v) for v in vs]
 
-        desired_oy = alpha_v * matrixmultiply(transpose(a_v), x_v[::2]) +
-        beta_v * y_v
+        desired_oy = alpha_v * matrixmultiply(transpose(a_v), x_v[::2]) + \
+            beta_v * y_v
 
         oy = alpha * T.dot(a.T, x[::2]) + beta * y