                  if not event.summary.value:
                      continue
                  if event.summary.value[0].tag == "keras":
                      self._parse_outputs(event)
                  if event.summary.value[0].tag.startswith("batch_"):
                      data[event.step] = self._process_event(event, data.get(event.step, {}))
  
          except tf_errors.DataLossError as err:
              logger.warning("The logs for Session %s are corrupted and cannot be displayed. "
 
</s> remove                     data[event.step] = self._process_event(event, data.get(event.step, dict()))
 </s> add                     data[event.step] = self._process_event(event, data.get(event.step, {})) </s> remove                 with open(self._config_file, "r") as cnf:
 </s> add                 with open(self._config_file, "r", encoding="utf8") as cnf: </s> remove                     req.add_header("Range", "bytes={}-".format(downloaded_size))
                response = urllib.request.urlopen(req, timeout=10)
                self.logger.debug("header info: {%s}", response.info())
                self.logger.debug("Return Code: %s", response.getcode())
                self._write_zipfile(response, downloaded_size)
 </s> add                     req.add_header("Range", f"bytes={downloaded_size}-")
                with urllib.request.urlopen(req, timeout=10) as response:
                    self.logger.debug("header info: {%s}", response.info())
                    self.logger.debug("Return Code: %s", response.getcode())
                    self._write_zipfile(response, downloaded_size) </s> remove         data = dict()
 </s> add         data = {}
                if get_backend() == "amd":
                    # No model is logged for AMD so need to get loss labels from state file
                    self._add_amd_loss_labels(session_id)
                  if not event.summary.value:
                      continue
                  if event.summary.value[0].tag == "keras":
                      self._parse_outputs(event)
                if get_backend() == "amd":
                    # No model is logged for AMD so need to get loss labels from state file
                    self._add_amd_loss_labels(session_id)
                  if event.summary.value[0].tag.startswith("batch_"):
                      data[event.step] = self._process_event(event, data.get(event.step, {}))
  
          except tf_errors.DataLossError as err:
              logger.warning("The logs for Session %s are corrupted and cannot be displayed. "
keep keep keep add 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 
lib/gui/analysis/event_reader.py