More pep8 / pyflakes

theano/compile/builders.py

-
 from theano import gof
 from theano import gradient as G
 from function_module import orig_function
@@ -33,16 +32,19 @@ class OpFromGraph(gof.Op):
       e2 = op(x, y, z) + op(z, y, x)
       fn = function([x, y, z], [e2])
     """
-    
-    def __init__(self, inputs, outputs, grad_depth = 1, **kwargs):
+
+    def __init__(self, inputs, outputs, grad_depth=1, **kwargs):
         if not isinstance(outputs, list):
             raise TypeError('outputs must be list', outputs)
         for i in inputs + outputs:
             if not isinstance(i, gof.Variable):
-                raise TypeError('inputs and outputs must be Variable instances', i)
+                raise TypeError(
+                        'inputs and outputs must be Variable instances', i)
         if 'updates' in kwargs:
             raise TypeError('updates are not allowed in kwargs')
-        # TODO: the graph may have implicit inputs like Value and SharedVariable instances.
+
+        # TODO: the graph may have implicit inputs like Value and
+        #       SharedVariable instances.
         #       what impact to they have on the validity of this Op?
         self.fn = orig_function(inputs, outputs, **kwargs)
         self.inputs = inputs
@@ -52,7 +54,8 @@ class OpFromGraph(gof.Op):
 
         if grad_depth > 0:
             output_grads = [t() for t in self.output_types]
-            gd = G.grad_sources_inputs(zip(self.outputs, output_grads), self.inputs)
+            gd = G.grad_sources_inputs(zip(self.outputs, output_grads),
+                    self.inputs)
             gs = map(gd.get, self.inputs)
             self.grad_ops = []
             for g in gs:
@@ -63,8 +66,9 @@ class OpFromGraph(gof.Op):
                     # to compute the gradient, so we ignore them.
                     self.grad_ops.append(OpFromGraph(inputs + output_grads,
                                                      [g],
-                                                     grad_depth = grad_depth - 1,
+                                                     grad_depth=grad_depth - 1,
                                                      on_unused_input='ignore'))
+
     def __eq__(self, other):
         #TODO: recognize a copy
         return self is other
@@ -76,7 +80,8 @@ class OpFromGraph(gof.Op):
     def make_node(self, *inputs):
         for input, type in zip(inputs, self.input_types):
             if not type == input.type:
-                raise TypeError("Wrong type, expected %s but got %s" % (type, input.type))
+                raise TypeError("Wrong type, expected %s but got %s"
+                        % (type, input.type))
         return gof.Apply(self,
                          inputs,
                          [type() for type in self.output_types])
@@ -85,8 +90,8 @@ class OpFromGraph(gof.Op):
         variables = self.fn(*inputs)
         assert len(variables) == len(outputs)
         for output, variable in zip(outputs, variables):
-            ##TODO: when function's output-borrowing semantics are correct, we wont need this
-            # copy anymore
+            ##TODO: when function's output-borrowing semantics are correct,
+            # we wont need this copy anymore
             output[0] = variable.copy()
 
     def grad(self, inputs, output_grads):
@@ -94,5 +99,3 @@ class OpFromGraph(gof.Op):
             return [go(*(inputs + output_grads)) for go in self.grad_ops]
         else:
             raise NotImplementedError
-
-