              For sides "a" and "b"; The list of samples, targets and masks as
              :class:`numpy.ndarrays` for creating a time-lapse frame
          """
          batchsizes = []
          samples = dict()
          images = dict()
          masks = dict()
          for side in ("a", "b"):
              batch = next(self._display_feeds["timelapse"][side])
              batchsizes.append(len(batch["samples"]))
              samples[side] = batch["samples"]
              images[side] = batch["targets"][-1]
 
</s> remove         retval = dict()
 </s> add         retval = {} </s> remove             self._samples = dict()
            self._target = dict()
            self._masks = dict()
 </s> add             self._samples = {}
            self._target = {}
            self._masks = {} </s> remove         self._funcs = dict()
 </s> add         self._funcs = {} </s> remove         feeds = dict()
        figures = dict()
        headers = dict()
 </s> add         feeds = {}
        figures = {}
        headers = {} </s> remove         self._target = dict()
        self._samples = dict()
        self._masks = dict()
 </s> add         self._target = {}
        self._samples = {}
        self._masks = {}
        samples = {}
        images = {}
        masks = {}
              For sides "a" and "b"; The list of samples, targets and masks as
              :class:`numpy.ndarrays` for creating a time-lapse frame
          """
          batchsizes = []
        samples = {}
        images = {}
        masks = {}
        samples = {}
        images = {}
        masks = {}
        samples = {}
        images = {}
        masks = {}
          for side in ("a", "b"):
              batch = next(self._display_feeds["timelapse"][side])
              batchsizes.append(len(batch["samples"]))
              samples[side] = batch["samples"]
              images[side] = batch["targets"][-1]
keep keep keep keep replace replace replace keep keep keep keep keep
 Update code to support Tensorflow versions up to 2.8 (#1213)

* Update maximum tf version in setup + requirements

* - bump max version of tf version in launcher
- standardise tf version check

* update keras get_custom_objects  for tf>2.6

* bugfix: force black text in GUI file dialogs (linux)

* dssim loss - Move to stock tf.ssim function

* Update optimizer imports for compatibility

* fix logging for tf2.8

* Fix GUI graphing for TF2.8

* update tests

* bump requirements.txt versions

* Remove limit on nvidia-ml-py

* Graphing bugfixes
  - Prevent live graph from displaying if data not yet available

* bugfix: Live graph. Collect loss labels correctly

* fix: live graph - swallow inconsistent loss errors

* Bugfix: Prevent live graph from clearing during training

* Fix graphing for AMD 
plugins/train/trainer/_base.py