message: in the tf.py example just pretend to train
  Training on random data doesn't really make sense and takes a lot of
  time and CPU, so it's hard to scale up the number of participants.
diff: @@ -83,11 +83,13 @@ class Participant(ParticipantABC):
  weights=training_input.weights, shapes=self.model_shapes
  )
  - # FIXME: the epoch should come from the aggregator but I don't
  - # understand what it is exactly. According to Jan it's only
  - # used for metrics so I think it's ok to hardcode this to 10.
  - for _ in range(0, 10):
  - self.model.fit(x=self.trainset, verbose=2, shuffle=False)
  + # Uncomment this if you want to train the model. However, this
  + # is a dummy example with random data so there's no point in
  + # actually training.
  + #
  + # epochs = 10
  + # for _ in range(0, epochs):
  + # self.model.fit(x=self.trainset, verbose=2, shuffle=False)
  # return the updated model weights and the number of training samples
  return TrainingResult(self.get_tensorflow_weights(), self.train_samples)
  