code: def _before_training_exp(self, **kwargs):
      super()._before_training_exp(drop_last=True, **kwargs)
  
      # Initialize alpha-lr parameters
      if not self.alpha_params_initialized:
          self.alpha_params_initialized = True
          # Iterate through model parameters and add the corresponding
          # alpha_lr parameter
          for n, p in self.model.named_parameters():
              alpha_param = nn.Parameter(
                  torch.ones(p.shape) * self.alpha_init,
                  requires_grad=True,
              )
              self.alpha_params[n.replace(".", "_")] = alpha_param
          self.alpha_params.to(self.device)
  
          # Create optimizer for the alpha_lr parameters
          self.optimizer_alpha = torch.optim.SGD(
              self.alpha_params.parameters(), lr=self.lr_alpha
          )
  
      # update alpha-lr parameters
      for n, p in self.model.named_parameters():
          n = n.replace(
              ".", "_"
          )  # dict does not support names with '.'
          if n in self.alpha_params:
              if self.alpha_params[n].shape != p.shape:
                  old_shape = self.alpha_params[n].shape
                  # parameter expansion
                  expanded = False
                  assert len(p.shape) == len(
                      old_shape
                  ), "Expansion cannot add new dimensions"
                  for i, (snew, sold) in enumerate(
                      zip(p.shape, old_shape)
                  ):
                      assert snew >= sold, "Shape cannot decrease."
                      if snew > sold:
                          assert not expanded, (
                              "Expansion cannot occur "
                              "in more than one dimension."
                          )
                          expanded = True
                          exp_idx = i
  
                  alpha_param = torch.ones(p.shape) * self.alpha_init
                  idx = [
                      slice(el)
                      if i != exp_idx
                      else slice(old_shape[exp_idx])
                      for i, el in enumerate(p.shape)
                  ]
                  alpha_param[idx] = (
                      self.alpha_params[n].detach().clone()
                  )
                  alpha_param = nn.Parameter(
                      alpha_param, requires_grad=True
                  )
                  self.alpha_params[n] = alpha_param
          else:
              # Add new alpha_lr for the new parameter
              alpha_param = nn.Parameter(
                  torch.ones(p.shape) * self.alpha_init,
                  requires_grad=True,
              )
              self.alpha_params[n] = alpha_param
  
          self.alpha_params.to(self.device)
          # Re-init optimizer for the new set of alpha_lr parameters
          self.optimizer_alpha = torch.optim.SGD(
              self.alpha_params.parameters(), lr=self.lr_alpha
          )
  
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 2 for calling the LaMAML obj's underlying member method code with example usage: obj._before_training_exp() without return types