code: def main(client, trial):
      # Create new model.
      model = define_model(trial.parameters)
  
      # Define dataset.
      gtrain = dataset_bianchini(batchsize=100, k=3)
      gvalid = dataset_bianchini(batchsize=100, k=3)
  
      model.fit_generator(
          gtrain,
          steps_per_epoch=100,
          validation_data=gvalid,
          validation_steps=10,
          callbacks=[
              client.keras_send_metrics(
                  trial,
                  objective_name="val_loss",
                  context_names=["val_acc"],
              )
          ],
          epochs=trial.parameters["epochs"],
          verbose=2,
      )
  
file_path: sherpa/examples/parallel-examples/bianchini/bianchini.py
imports: from __future__ import print_function
  import numpy
  import sherpa
  from keras.models import Model
  from keras.layers import Dense, Input
  from keras.optimizers import SGD
label_desc: Use image node_id 3 for calling a global function with example usage: main(client, trial) without return types