Simple gist on how to train a meta-learner in PyTorch

meta_train.py

def train(forward_model, backward_model, optimizer, meta_optimizer, train_data, meta_epochs):
  """ Train a meta-learner
  Inputs:
    forward_model, backward_model: Two identical PyTorch modules (can have shared Tensors)
    optimizer: a neural net to be used as optimizer (an instance of the MetaLearner class)
    meta_optimizer: an optimizer for the optimizer neural net, e.g. ADAM
    train_data: an iterator over an epoch of training data
    meta_epochs: meta-training steps
  To be added: intialization, early stopping, checkpointing, more control over everything
  """
  for meta_epoch in range(meta_epochs): # Meta-training loop (train the optimizer)
    optimizer.zero_grad()
    losses = []
    for inputs, labels in train_data:   # Meta-forward pass (train the model)
      forward_model.zero_grad()         # Forward pass
      inputs = Variable(inputs)
      labels = Variable(labels)
      output = forward_model(inputs)
      loss = loss_func(output, labels)  # Compute loss
      losses.append(loss)
      loss.backward()                   # Backward pass to add gradients to the forward_model
      optimizer(forward_model,          # Optimizer step (update the models)
                backward_model)
    meta_loss = sum(losses)             # Compute a simple meta-loss
    meta_loss.backward()                # Meta-backward pass
    meta_optimizer.step()               # Meta-optimizer step

Excuse me, but the code in the 22nd line is optimizer(forward_model, backward_model) or optimizer.forward(forward_model, backward_model)?