          if isinstance(feed, np.ndarray):
              feed = [feed]
          items = feed[0].shape[0]
          done_items = 0
          results = list()
          while done_items < items:
              if batch_size < 4:  # Not much difference in BS < 4
                  batch_size = 1
              batch_items = ((items - done_items) // batch_size) * batch_size
              if batch_items:
 
</s> remove         ret = list()
 </s> add         ret = [] </s> remove         retained_box_indices = list()
 </s> add         retained_box_indices = [] </s> remove         retval = list()
 </s> add         retval = [] </s> remove                 ret.append(list())
 </s> add                 ret.append([]) </s> remove         return KerasModel(input_, var_x, name="inter_{}".format(side))
 </s> add         return KerasModel(input_, var_x, name=f"inter_{side}")
        results = []
          if isinstance(feed, np.ndarray):
              feed = [feed]
          items = feed[0].shape[0]
          done_items = 0
        results = []
          while done_items < items:
              if batch_size < 4:  # Not much difference in BS < 4
                  batch_size = 1
              batch_items = ((items - done_items) // batch_size) * batch_size
              if batch_items:
keep keep keep keep replace keep keep keep keep keep
 Update all Keras Imports to be conditional (#1214)

* Remove custom keras importer

* first round keras imports fix

* launcher.py: Remove KerasFinder references

* 2nd round keras imports update (lib and extract)

* 3rd round keras imports update (train)

* remove KerasFinder from tests

* 4th round keras imports update (tests) 
lib/model/session.py