update by flake8

pytorch_lightning/root_module/grads.py

@@ -17,11 +17,13 @@ class GradInformation(nn.Module):
                     total_norm += param_norm ** norm_type
                     norm = param_norm ** (1 / norm_type)
 
-                    results['grad_{}_norm_{}'.format(norm_type, i)] = round(norm.data.cpu().numpy().flatten()[0], 3)
+                    grad = round(norm.data.cpu().numpy().flatten()[0], 3)
+                    results['grad_{}_norm_{}'.format(norm_type, i)] = grad
                 except Exception:
                     # this param had no grad
                     pass
 
         total_norm = total_norm ** (1. / norm_type)
-        results['grad_{}_norm_total'.format(norm_type)] = round(total_norm.data.cpu().numpy().flatten()[0], 3)
+        grad = round(total_norm.data.cpu().numpy().flatten()[0], 3)
+        results['grad_{}_norm_total'.format(norm_type)] = grad
         return results

pytorch_lightning/root_module/model_saving.py

@@ -3,7 +3,8 @@ import re
 
 import torch
 
-from ..pt_overrides.override_data_parallel import LightningDistributedDataParallel, LightningDataParallel
+from ..pt_overrides.override_data_parallel import (
+    LightningDistributedDataParallel, LightningDataParallel)
 
 
 class ModelIO(object):
@@ -45,7 +46,8 @@ class ModelIO(object):
 class TrainerIO(object):
 
     def __get_model(self):
-        is_dp_module = type(self.model) is LightningDistributedDataParallel or type(self.model) is LightningDataParallel
+        is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,
+                                               LightningDataParallel))
         model = self.model.module if is_dp_module else self.model
         return model