message: Rename B to batch_size for unitary training
diff: @@ -54,7 +54,7 @@ def run_unitary_versus_federated(xy_splits, xy_val, xy_test, C):
  partition_id = 0
  logging.info("> Train model on partition {}".format(partition_id))
  ul_results = run_uni(
  - xy_splits[partition_id], xy_val, xy_test, epochs=ROUNDS, B=FLH_B
  + xy_splits[partition_id], xy_val, xy_test, epochs=ROUNDS, batch_size=FLH_B
  )
  # Train CNN using federated learning on all partitions
  @@ -75,12 +75,12 @@ def run_uni(
  xy_val: Tuple[np.ndarray, np.ndarray],
  xy_test: Tuple[np.ndarray, np.ndarray],
  epochs: int,
  - B: int,
  + batch_size: int,
  ):
  # Initialize model and participant
  model = orig_cnn_compiled()
  participant = Participant(
  - model, xy_train=xy_train, xy_val=xy_val, num_classes=10, batch_size=B
  + model, xy_train=xy_train, xy_val=xy_val, num_classes=10, batch_size=batch_size
  )
  # Train model
  history = participant._train(epochs) # pylint: disable-msg=protected-access
  