parameters: (self, trial, objective_name, context_names=[])
return_statement: return keras.callbacks.LambdaCallback(on_epoch_end=send_call)
docstring: Keras Callbacks to send metrics to SHERPA.
  
          Args:
              trial (sherpa.core.Trial): trial to send metrics for.
              objective_name (str): the name of the objective e.g. ``loss``,
                  ``val_loss``, or any of the submitted metrics.
              context_names (list[str]): names of all other metrics to be
                  monitored.
docstring_summary: Keras Callbacks to send metrics to SHERPA.
docstring_tokens: Keras Callbacks to send metrics to SHERPA .
function: def keras_send_metrics(self, trial, objective_name, context_names=[]):
          """
          Keras Callbacks to send metrics to SHERPA.
  
          Args:
              trial (sherpa.core.Trial): trial to send metrics for.
              objective_name (str): the name of the objective e.g. ``loss``,
                  ``val_loss``, or any of the submitted metrics.
              context_names (list[str]): names of all other metrics to be
                  monitored.
          """        
          import keras.callbacks
          send_call = lambda epoch, logs: self.send_metrics(trial=trial,
                                                            iteration=epoch,
                                                            objective=logs[objective_name],
                                                            context={n: logs[n] for n in context_names})
          return keras.callbacks.LambdaCallback(on_epoch_end=send_call)
function_tokens: def keras_send_metrics ( self , trial , objective_name , context_names = [ ] ) : import keras . callbacks send_call = lambda epoch , logs : self . send_metrics ( trial = trial , iteration = epoch , objective = logs [ objective_name ] , context = { n : logs [ n ] for n in context_names } ) return keras . callbacks . LambdaCallback ( on_epoch_end = send_call )