flake8 for tensor/xlogx.py

theano/tensor/xlogx.py

@@ -13,12 +13,15 @@ class XlogX(scalar.UnaryScalarOp):
         if x == 0.0:
             return 0.0
         return x * numpy.log(x)
+
     def impl(self, x):
         return XlogX.st_impl(x)
+
     def grad(self, inputs, grads):
         x, = inputs
         gz, = grads
         return [gz * (1 + scalar.log(x))]
+
     def c_code(self, node, name, inputs, outputs, sub):
         x, = inputs
         z, = outputs
@@ -28,6 +31,7 @@ class XlogX(scalar.UnaryScalarOp):
                 ? 0.0
                 : %(x)s * log(%(x)s);""" % locals()
         raise NotImplementedError('only floatingpoint is implemented')
+
 scalar_xlogx = XlogX(scalar.upgrade_to_float, name='scalar_xlogx')
 xlogx = Elemwise(scalar_xlogx, name='xlogx')
 
@@ -41,12 +45,15 @@ class XlogY0(scalar.BinaryScalarOp):
         if x == 0.0:
             return 0.0
         return x * numpy.log(y)
+
     def impl(self, x, y):
         return XlogY0.st_impl(x, y)
+
     def grad(self, inputs, grads):
         x, y = inputs
         gz, = grads
         return [gz * scalar.log(y), gz * x / y]
+
     def c_code(self, node, name, inputs, outputs, sub):
         x, y = inputs
         z, = outputs
@@ -56,5 +63,6 @@ class XlogY0(scalar.BinaryScalarOp):
                 ? 0.0
                 : %(x)s * log(%(y)s);""" % locals()
         raise NotImplementedError('only floatingpoint is implemented')
+
 scalar_xlogy0 = XlogY0(scalar.upgrade_to_float, name='scalar_xlogy0')
 xlogy0 = Elemwise(scalar_xlogy0, name='xlogy0')

theano/tests/test_flake8.py

@@ -57,7 +57,6 @@ whitelist_flake8 = [
     "typed_list/tests/test_type.py",
     "typed_list/tests/test_opt.py",
     "typed_list/tests/test_basic.py",
-    "tensor/xlogx.py",
     "tensor/blas_headers.py",
     "tensor/utils.py",
     "tensor/type.py",