Why does MAML need copy_initial_weights=False?

I was going through the example (https://github.com/facebookresearch/higher/blob/master/examples/maml-omniglot.py) and it used:

with higher.innerloop_ctx( net, inner_opt, copy_initial_weights=False) as (fnet, diffopt):

why does MAML require that?

Simplified code snippet:

def train(db, net, device, meta_opt, epoch, log):
    n_train_iter = db.x_train.shape[0] // db.batchsz

    for batch_idx in range(n_train_iter):
        start_time = time.time()
        # Sample a batch of support and query images and labels.
        x_spt, y_spt, x_qry, y_qry = db.next()

        task_num, setsz, c_, h, w = x_spt.size()

        # Initialize the inner optimizer to adapt the parameters to
        # the support set.
        n_inner_iter = 5
        inner_opt = torch.optim.SGD(net.parameters(), lr=1e-1)

        for i in range(task_num):
            with higher.innerloop_ctx( net, inner_opt, copy_initial_weights=False) as (fnet, diffopt):
                # Optimize the likelihood of the support set by taking
                # gradient steps w.r.t. the model's parameters.
                # This adapts the model's meta-parameters to the task.
                # higher is able to automatically keep copies of
                # your network's parameters as they are being updated.
                for _ in range(n_inner_iter):
                    spt_logits = fnet(x_spt[i])
                    spt_loss = F.cross_entropy(spt_logits, y_spt[i])

                # The final set of adapted parameters will induce some
                # final loss and accuracy on the query dataset.
                # These will be used to update the model's meta-parameters.
                qry_logits = fnet(x_qry[i])
                qry_loss = F.cross_entropy(qry_logits, y_qry[i])

                # Update the model's meta-parameters to optimize the query
                # losses across all of the tasks sampled in this batch.
                # This unrolls through the gradient steps.



all links to copy initial weights stuff: