flake8 fixes

theano/tensor/opt.py

@@ -1596,12 +1596,12 @@ def local_useless_elemwise(node):
         elif node.op.scalar_op == theano.scalar.add and len(node.inputs) == 1:
             # No need to copy over any stack trace
             return [node.inputs[0]]
-        elif node.op.scalar_op == theano.scalar.identity and \
-             len(node.inputs) == 1:
+        elif (node.op.scalar_op == theano.scalar.identity and
+              len(node.inputs) == 1):
             return [node.inputs[0]]
 
-        elif isinstance(node.op.scalar_op, scalar.AND) and \
-             len(node.inputs) == 2:
+        elif (isinstance(node.op.scalar_op, scalar.AND) and
+              len(node.inputs) == 2):
 
             if isinstance(node.inputs[0], T.TensorConstant):
                 const_val = T.extract_constant(node.inputs[0])
@@ -1617,8 +1617,8 @@ def local_useless_elemwise(node):
                 elif const_val == 0:
                     return zeros_like(node, 0)
 
-        elif isinstance(node.op.scalar_op, scalar.OR) and \
-             len(node.inputs) == 2:
+        elif (isinstance(node.op.scalar_op, scalar.OR) and
+              len(node.inputs) == 2):
 
             if isinstance(node.inputs[0], T.TensorConstant):
                 const_val = T.extract_constant(node.inputs[0])
@@ -1634,8 +1634,8 @@ def local_useless_elemwise(node):
                 if const_val == 1:
                     return ones_like(node, 0)
 
-        elif isinstance(node.op.scalar_op, scalar.XOR) and \
-             len(node.inputs) == 2:
+        elif (isinstance(node.op.scalar_op, scalar.XOR) and
+              len(node.inputs) == 2):
             if node.inputs[0] is node.inputs[1]:
                 return zeros_like(node, 0)
 
@@ -2817,10 +2817,11 @@ def local_inplace_setsubtensor(node):
 
     """
     if isinstance(node.op, IncSubtensor) and not node.op.inplace:
+        dta = node.op.destroyhandler_tolerate_aliased
         new_op = node.op.__class__(
             node.op.idx_list, inplace=True,
             set_instead_of_inc=node.op.set_instead_of_inc,
-            destroyhandler_tolerate_aliased=node.op.destroyhandler_tolerate_aliased)
+            destroyhandler_tolerate_aliased=dta)
         new_node = new_op(*node.inputs)
         return [new_node]
     return False
@@ -3297,14 +3298,14 @@ def local_useless_switch(node):
         left = node.inputs[1]
         right = node.inputs[2]
         cond_var = node.inputs[0]
-        if (cond_var.owner and
-            isinstance(cond_var.owner.op, T.Elemwise) and
-            isinstance(cond_var.owner.op.scalar_op, scalar.LE) and
-            cond_var.owner.inputs[0].owner and
-            isinstance(cond_var.owner.inputs[0].owner.op, Shape_i) and
-            T.extract_constant(cond_var.owner.inputs[1]) == 0 and
-            T.extract_constant(left) == 0 and
-            right is cond_var.owner.inputs[0]):
+        if cond_var.owner and \
+           isinstance(cond_var.owner.op, T.Elemwise) and \
+           isinstance(cond_var.owner.op.scalar_op, scalar.LE) and \
+           cond_var.owner.inputs[0].owner and \
+           isinstance(cond_var.owner.inputs[0].owner.op, Shape_i) and \
+           T.extract_constant(cond_var.owner.inputs[1]) == 0 and \
+           T.extract_constant(left) == 0 and \
+           right is cond_var.owner.inputs[0]:
             assert right.type == node.outputs[0].type
             return [right]
         return False
@@ -4217,7 +4218,6 @@ def local_useless_elemwise_comparison(node):
     :note: These cases appear in the graph generated by scan.
            These optimizations will not reduce computation,
            but will make the graph easier to read.
-    
     # Comparing to itself is constant
     Elemwise[{LT,GT}](X, X) -> Elemwise[zeros](X)
     Elemwise[{LE,GE}](X, X) -> Elemwise[ones](X)
@@ -4241,72 +4241,72 @@ def local_useless_elemwise_comparison(node):
     if node.op.scalar_op.nin != 2:
         return
     # Elemwise[{LT,GT}](X, X) -> Elemwise[zeros](X)
-    if (isinstance(node.op.scalar_op, (scalar.LT, scalar.GT)) and
-        node.inputs[0] is node.inputs[1]):
+    if isinstance(node.op.scalar_op, (scalar.LT, scalar.GT)) and \
+       node.inputs[0] is node.inputs[1]:
         return [T.zeros_like(node.outputs[0], dtype=node.outputs[0].type.dtype)]
     # Elemwise[{LE,GE}](X, X) -> Elemwise[ones](X)
-    if (isinstance(node.op.scalar_op, (scalar.LE, scalar.GE)) and
-        node.inputs[0] is node.inputs[1]):
+    if isinstance(node.op.scalar_op, (scalar.LE, scalar.GE)) and \
+       node.inputs[0] is node.inputs[1]:
         return [T.ones_like(node.inputs[0], dtype=node.outputs[0].type.dtype)]
     # Elemwise[{minimum,maximum}](X, X) -> X
-    if (isinstance(node.op.scalar_op, (scalar.Minimum, scalar.Maximum)) and
-        node.inputs[0] is node.inputs[1]):
+    if isinstance(node.op.scalar_op, (scalar.Minimum, scalar.Maximum)) and \
+       node.inputs[0] is node.inputs[1]:
         return [node.inputs[0]]
 
     # Elemwise[LT](X.shape[i], 0) -> Elemwise[zeros](X)
-    if (isinstance(node.op.scalar_op, scalar.LT) and
-        node.inputs[0].owner and
-        isinstance(node.inputs[0].owner.op, Shape_i) and
-        T.extract_constant(node.inputs[1]) == 0):
+    if isinstance(node.op.scalar_op, scalar.LT) and \
+       node.inputs[0].owner and \
+       isinstance(node.inputs[0].owner.op, Shape_i) and \
+       T.extract_constant(node.inputs[1]) == 0:
         return [T.zeros_like(node.outputs[0])]
     # Elemwise[GE](X.shape[i], 0) -> Elemwise[ones](X)
-    if (isinstance(node.op.scalar_op, scalar.GE) and
-        node.inputs[0].owner and
-        isinstance(node.inputs[0].owner.op, Shape_i) and
-        T.extract_constant(node.inputs[1]) == 0):
+    if isinstance(node.op.scalar_op, scalar.GE) and \
+       node.inputs[0].owner and \
+       isinstance(node.inputs[0].owner.op, Shape_i) and \
+       T.extract_constant(node.inputs[1]) == 0:
         return [T.ones_like(node.outputs[0])]
     # Elemwise[maximum](X.shape[i], 0) -> X.shape[i]
-    if (isinstance(node.op.scalar_op, scalar.Maximum) and
-        node.inputs[0].owner and
-        isinstance(node.inputs[0].owner.op, Shape_i) and
-        T.extract_constant(node.inputs[1]) == 0):
+    if isinstance(node.op.scalar_op, scalar.Maximum) and \
+       node.inputs[0].owner and \
+       isinstance(node.inputs[0].owner.op, Shape_i) and \
+       T.extract_constant(node.inputs[1]) == 0:
         return [node.inputs[0]]
     # Elemwise[maximum](0, X.shape[i]) -> X.shape[i]
-    if (isinstance(node.op.scalar_op, scalar.Maximum) and
-        T.extract_constant(node.inputs[0]) == 0 and
-        node.inputs[1].owner and
-        isinstance(node.inputs[1].owner.op, Shape_i)):
+    if isinstance(node.op.scalar_op, scalar.Maximum) and \
+       T.extract_constant(node.inputs[0]) == 0 and \
+       node.inputs[1].owner and \
+       isinstance(node.inputs[1].owner.op, Shape_i):
         return [node.inputs[1]]
     # Elemwise[minimum](X.shape[i], 0) -> 0
-    if (isinstance(node.op.scalar_op, scalar.Minimum) and
-        node.inputs[0].owner and
-        isinstance(node.inputs[0].owner.op, Shape_i) and
-        T.extract_constant(node.inputs[1]) == 0):
+    if isinstance(node.op.scalar_op, scalar.Minimum) and \
+       node.inputs[0].owner and \
+       isinstance(node.inputs[0].owner.op, Shape_i) and \
+       T.extract_constant(node.inputs[1]) == 0:
         return [T.zeros_like(node.outputs[0])]
     # Elemwise[minimum](0, X.shape[i]) -> 0
-    if (isinstance(node.op.scalar_op, scalar.Minimum) and
-        T.extract_constant(node.inputs[0]) == 0 and
-        node.inputs[1].owner and
-        isinstance(node.inputs[1].owner.op, Shape_i)):
+    if isinstance(node.op.scalar_op, scalar.Minimum) and \
+       T.extract_constant(node.inputs[0]) == 0 and \
+       node.inputs[1].owner and \
+       isinstance(node.inputs[1].owner.op, Shape_i):
         return [T.zeros_like(node.outputs[0])]
 
     # Elemwise[LT](sum([anything that is shapes]), 0) -> Elemwise[zeros](X)
-    if (isinstance(node.op.scalar_op, scalar.LT) and
-        node.inputs[0].owner and
-        isinstance(node.inputs[0].owner.op, Elemwise) and
-        isinstance(node.inputs[0].owner.op, scalar.Add) and
-        all([isinstance(var.owner and var.owner.op, Shape_i)
-             for var in node.inputs[0].owner.inputs]) and
-        T.extract_constant(node.inputs[1]) == 0):
+    if isinstance(node.op.scalar_op, scalar.LT) and \
+       node.inputs[0].owner and \
+       isinstance(node.inputs[0].owner.op, Elemwise) and \
+       isinstance(node.inputs[0].owner.op, scalar.Add) and \
+       all([isinstance(var.owner and var.owner.op, Shape_i)
+            for var in node.inputs[0].owner.inputs]) and \
+       T.extract_constant(node.inputs[1]) == 0:
         return [T.zeros_like(node.inputs[0], dtype=node.outputs[0].dtype)]
     # Elemwise[GE](sum([anything that is shapes]), 0) -> Elemwise[ones](X)
-    if (isinstance(node.op.scalar_op, scalar.GE) and
-        node.inputs[0].owner and
-        isinstance(node.inputs[0].owner.op, Elemwise) and
-        isinstance(node.inputs[0].owner.op, scalar.Add) and
-        all([isinstance(var.owner and var.owner.op, Shape_i)
-             for var in node.inputs[0].owner.inputs]) and
-        T.extract_constant(node.inputs[1]) == 0):
+    if isinstance(node.op.scalar_op, scalar.GE) and \
+       node.inputs[0].owner and \
+       isinstance(node.inputs[0].owner.op, Elemwise) and \
+       isinstance(node.inputs[0].owner.op, scalar.Add) and \
+       all([isinstance(var.owner and var.owner.op, Shape_i)
+            for var in node.inputs[0].owner.inputs]) and \
+       T.extract_constant(node.inputs[1]) == 0:
         return [T.ones_like(node.inputs[0], dtype=node.outputs[0].dtype)]
 
     return