Flake8 for compile/builders.py

theano/compile/builders.py

@@ -15,7 +15,9 @@ class OpFromGraph(gof.Op):
 
     TODO:
         - examples for a multi-layer mlp. where?
-        - __hash__, __eq__ otherwise won't merge, try gof.opt.is_same_graph_with_merge(op1.new_outputs, op2, new_outputs)
+        - __hash__, __eq__ otherwise won't merge, try
+          gof.opt.is_same_graph_with_merge(op1.new_outputs, op2,
+          new_outputs)
         - c_code() to remove the double overhead?
         - opt to unfold it, work inplace on inputs
         - grad() make it support DisconnectedType and the new interface
@@ -68,7 +70,7 @@ class OpFromGraph(gof.Op):
         for i in inputs + outputs:
             if not isinstance(i, gof.Variable):
                 raise TypeError(
-                        'inputs and outputs must be Variable instances', i)
+                    'inputs and outputs must be Variable instances', i)
         if 'updates' in kwargs:
             raise TypeError('updates are not allowed in kwargs')
 
@@ -76,8 +78,6 @@ class OpFromGraph(gof.Op):
         # not see them. Otherwise their is problem with the gradient.
         self.shared_inputs = [var for var in gof.graph.inputs(outputs)
                               if isinstance(var, SharedVariable)]
-        used_inputs = [var for var in gof.graph.inputs(outputs)
-                       if not isinstance(var, gof.Constant)]
         shared_vars = [var.type() for var in self.shared_inputs]
         new = rebuild_collect_shared(outputs, inputs=inputs + shared_vars,
                                      replace=dict(zip(self.shared_inputs,
@@ -110,8 +110,8 @@ 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,
                          list(inputs) + self.shared_inputs,
                          [type() for type in self.output_types])
@@ -143,9 +143,10 @@ class OpFromGraph(gof.Op):
             grad_ops = self.grad_ops
         else:
             gs = theano.gradient.grad(cost=None,
-                        known_grads=dict(zip(self.new_outputs, output_grads)),
-                        wrt=self.new_inputs,
-                        disconnected_inputs='ignore')
+                                      known_grads=dict(zip(self.new_outputs,
+                                                           output_grads)),
+                                      wrt=self.new_inputs,
+                                      disconnected_inputs='ignore')
 
             grad_ops = []
             for g in gs:

theano/tests/test_flake8.py

@@ -38,7 +38,6 @@ whitelist_flake8 = [
     "tests/test_tutorial.py",
     "tests/disturb_mem.py",
     "tests/unittest_tools.py",
-    "compile/builders.py",
     "compile/__init__.py",
     "compile/profiling.py",
     "compile/function_module.py",