code: def _outer_update(self, **kwargs):
      # Compute meta-gradient for the main model
      meta_loss = sum(self.meta_losses) / len(self.meta_losses)
      meta_grad_model = torch.autograd.grad(
          meta_loss,
          self.fast_model.parameters(time=0),
          retain_graph=True,
          allow_unused=True,
      )
      self.model.zero_grad()
      self.apply_grad(self.model, meta_grad_model)
  
      # Clip gradients
      torch.nn.utils.clip_grad_norm_(
          self.model.parameters(), self.grad_clip_norm
      )
  
      if self.learn_lr:
          # Compute meta-gradient for alpha-lr parameters
          meta_grad_alpha = torch.autograd.grad(
              meta_loss,
              self.alpha_params.parameters(),
              allow_unused=True,
          )
          self.alpha_params.zero_grad()
          self.apply_grad(self.alpha_params, meta_grad_alpha)
  
          torch.nn.utils.clip_grad_norm_(
              self.alpha_params.parameters(), self.grad_clip_norm
          )
          self.optimizer_alpha.step()
  
      # If sync-update: update with self.optimizer
      # o.w: use the learned LRs to update the model
      if self.sync_update:
          self.optimizer.step()
      else:
          for p, alpha in zip(
              self.model.parameters(), self.alpha_params.parameters()
          ):
              # Use relu on updated LRs to avoid negative values
              p.data = p.data - p.grad * F.relu(alpha)
  
      self.loss = meta_loss
  
imports: from typing import Callable, Sequence, Optional, Union
  import torch
  import warnings
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module, CrossEntropyLoss
  from torch.optim import Optimizer
  import math
  from avalanche.training.plugins import SupervisedPlugin, EvaluationPlugin
  from avalanche.training.plugins.evaluation import default_evaluator
  from avalanche.training.templates import SupervisedMetaLearningTemplate
  from avalanche.models.utils import avalanche_forward
label_desc: Use image node_id 6 for calling the LaMAML obj's underlying member method code with example usage: obj._outer_update() without return types