docstr: Implementation of Look-ahead MAML (LaMAML) strategy.
  
  :param model: PyTorch model.
  :param optimizer: PyTorch optimizer.
  :param criterion: loss function.
  :param n_inner_updates: number of inner updates.
  :param second_order: If True, it computes the second-order derivative
         of the inner update trajectory for the meta-loss. Otherwise,
         it computes the meta-loss with a first-order approximation.
  :param grad_clip_norm: gradient clipping norm.
  :param learn_lr: if True, it learns the LR for each batch of data.
  :param lr_alpha: LR for learning the main update's learning rate.
  :param sync_update: if True, it updates the meta-model with a fixed
                      learning rate. Mutually exclusive with learn_lr and
                      lr_alpha.
  :param alpha_init: initialization value for learnable LRs.
  :param max_buffer_size: maximum buffer size. The default storage
         policy is reservoir-sampling.
  :param buffer_mb_size: number of buffer samples in each step.
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code: def __init__(
      self,
      model: Module,
      optimizer: Optimizer,
      criterion=CrossEntropyLoss(),
      n_inner_updates: int = 5,
      second_order: bool = True,
      grad_clip_norm: float = 1.0,
      learn_lr: bool = True,
      lr_alpha: float = 0.25,
      sync_update: bool = False,
      alpha_init: float = 0.1,
      max_buffer_size: int = 200,
      buffer_mb_size: int = 10,
      train_mb_size: int = 1,
      train_epochs: int = 1,
      eval_mb_size: int = 1,
      device: Union[str, torch.device] = "cpu",
      plugins: Optional[Sequence["SupervisedPlugin"]] = None,
      evaluator: Union[
          EvaluationPlugin, Callable[[], EvaluationPlugin]
      ] = default_evaluator,
      eval_every=-1,
      peval_mode="epoch",
  ):
      """Implementation of Look-ahead MAML (LaMAML) strategy.
  
      :param model: PyTorch model.
      :param optimizer: PyTorch optimizer.
      :param criterion: loss function.
      :param n_inner_updates: number of inner updates.
      :param second_order: If True, it computes the second-order derivative
             of the inner update trajectory for the meta-loss. Otherwise,
             it computes the meta-loss with a first-order approximation.
      :param grad_clip_norm: gradient clipping norm.
      :param learn_lr: if True, it learns the LR for each batch of data.
      :param lr_alpha: LR for learning the main update's learning rate.
      :param sync_update: if True, it updates the meta-model with a fixed
                          learning rate. Mutually exclusive with learn_lr and
                          lr_alpha.
      :param alpha_init: initialization value for learnable LRs.
      :param max_buffer_size: maximum buffer size. The default storage
             policy is reservoir-sampling.
      :param buffer_mb_size: number of buffer samples in each step.
      """
      super().__init__(
          model,
          optimizer,
          criterion,
          train_mb_size,
          train_epochs,
          eval_mb_size,
          device,
          plugins,
          evaluator,
          eval_every,
          peval_mode,
      )
      self.n_inner_updates = n_inner_updates
      self.second_order = second_order
      self.grad_clip_norm = grad_clip_norm
      self.learn_lr = learn_lr
      self.lr_alpha = lr_alpha
      self.sync_update = sync_update
      self.alpha_init = alpha_init
      self.alpha_params: nn.ParameterDict = nn.ParameterDict()
      self.alpha_params_initialized: bool = False
      self.meta_losses: List[Tensor] = []
  
      self.buffer = Buffer(
          max_buffer_size=max_buffer_size,
          buffer_mb_size=buffer_mb_size,
          device=device,
      )
  
      self.model.apply(init_kaiming_normal)
  
imports: from typing import Callable, List, Sequence, Optional, Union
  from packaging.version import parse
  import warnings
  import torch
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module, CrossEntropyLoss
  from torch.optim import Optimizer
  from torch import Tensor
  import math
  from copy import deepcopy
  from avalanche.training.plugins import SupervisedPlugin, EvaluationPlugin
  from avalanche.training.plugins.evaluation import default_evaluator
  from avalanche.training.templates import SupervisedMetaLearningTemplate
  from avalanche.training.storage_policy import ReservoirSamplingBuffer
label_desc: Use image node_id 1 to create a new LaMAML object from inherited base classes: SupervisedMetaLearningTemplate with example: obj = LaMAML(model, optimizer, criterion, n_inner_updates, second_order, grad_clip_norm, learn_lr, lr_alpha, sync_update, alpha_init, max_buffer_size, buffer_mb_size, train_mb_size, train_epochs, eval_mb_size, device, plugins, evaluator, eval_every, peval_mode)