pep8 and flake8 checking

theano/tensor/sort.py

@@ -62,11 +62,15 @@ class SortOp(theano.Op):
             self, 0, axis,
             "Currently, we only implement the gradient on sort for vector"
             " matrix (and axis is None or 0) and tensor3")
-        if (isinstance(axis, theano.Constant) or (isinstance(axis, theano.tensor.TensorVariable) and axis.ndim==0)) and axis.data is not None:
+        if ((isinstance(axis, theano.Constant) or (isinstance(axis,
+                                                   theano.tensor.
+                                                   TensorVariable)
+                                                   and axis.ndim == 0)) and
+                axis.data is not None):
             indices = self.__get_argsort_indices(a, axis)
             inp_grad = output_grads[0][tuple(indices)]
         elif (axis is None or
-            (isinstance(axis, theano.Constant) and axis.data is None)):
+                (isinstance(axis, theano.Constant) and axis.data is None)):
             rev_idx = self.__get_argsort_indices(a, axis)
             inp_grad = output_grads[0][rev_idx].reshape(a.shape)
         axis_grad = theano.gradient.grad_undefined(
@@ -78,8 +82,9 @@ class SortOp(theano.Op):
     def __get_expanded_dim(self, a, axis, i):
         index_shape = [1] * a.ndim
         index_shape[i] = a.shape[i]
-        # it's a way to emulate numpy.ogrid[0: a.shape[0], 0: a.shape[1], 0: a.shape[2]]
-        index_val = theano.tensor.arange(a.shape[i]).reshape(index_shape)
+        # it's a way to emulate
+        # numpy.ogrid[0: a.shape[0], 0: a.shape[1], 0: a.shape[2]]
+        index_val = arange(a.shape[i]).reshape(index_shape)
         return index_val
 
     def __get_argsort_indices(self, a, axis):
@@ -100,9 +105,13 @@ class SortOp(theano.Op):
                 (isinstance(axis, theano.Constant) and axis.data is None)):
             return rev_idx
         indices = []
-        axis_data = theano.tensor.switch(theano.tensor.ge(axis.data, 0), axis.data, a.ndim + axis.data)
+        axis_data = theano.tensor.switch(theano.tensor.ge(axis.data, 0),
+                                         axis.data, a.ndim + axis.data)
         for i in range(a.ndim):
-            index_val = theano.tensor.switch(theano.tensor.eq(i, axis_data), rev_idx, self.__get_expanded_dim(a, axis, i))
+            index_val = theano.tensor.switch(theano.tensor.eq(i, axis_data),
+                                             rev_idx,
+                                             self.__get_expanded_dim(a,
+                                                                     axis, i))
             indices.append(index_val)
         return indices
     """