                  if last_step == -1:
                      last_step = event.step if live_data else 0
  
                  if event.step != last_step:
                      if last_step != 0:
                          loss.append(step)
                      step = []
 
</s> remove                     loss.append(step)
 </s> add                     if last_step != 0:
                        loss.append(step) </s> remove         loss = np.array(loss, dtype="float32")
        timestamps = np.array(timestamps, dtype="float64")
 </s> add         try:
            loss = np.array(loss, dtype="float32")
        except ValueError as err:
            # When collecting live loss, the current batch may not be completely populated
            # Carry over the last loss to the next collection
            if "setting an array element with a sequence" in str(err):
                carry_over = loss[-1]
                logger.debug("Carrying over data: (carry_over: %s, new loss: %s)",
                             carry_over, loss[:-1])
                loss = np.array(loss[:-1], dtype="float32")
            else:
                raise
                if live_data and self._cache[session_id].get("carry_over"):
                    step = self._cache[session_id]["carry_over"]
                    logger.debug("Retrieving carried over data: %s", step)
                    self._cache[session_id]["carry_over"] = None

                  if last_step == -1:
                      last_step = event.step if live_data else 0
  
                if live_data and self._cache[session_id].get("carry_over"):
                    step = self._cache[session_id]["carry_over"]
                    logger.debug("Retrieving carried over data: %s", step)
                    self._cache[session_id]["carry_over"] = None

                  if event.step != last_step:
                      if last_step != 0:
                          loss.append(step)
                      step = []
keep keep add keep keep keep keep
 Update to TF2.3 (#1100)

* Minimum requirements to tf2.3.
  - Handle upgrades for Windows users with tf2.2 installed by Pip
  - Handle windows upgrade from pip tf2.2
  - Explicitly install Cuda for Conda installs

* Update tensorflow errors api reference
* Suppress AutoGraph warning messages
* Update GUI Stats to work with tf2.3
* Fix live graph for tf2.3
* DSSIMObjective - autoGraph bugfix
* Update Travis test 
lib/gui/stats.py