code: def adapt_classification_layer(
      model: Module, num_classes: int, bias: Optional[bool] = None
  ) -> Tuple[str, Linear]:
      last_fc_layer: Linear
      last_fc_name, last_fc_layer = get_last_fc_layer(model)
  
      if bias is not None:
          use_bias = bias
      else:
          use_bias = last_fc_layer.bias is not None
  
      new_fc = Linear(
          last_fc_layer.in_features, num_classes, bias=use_bias
      )
      swap_last_fc_layer(model, new_fc)
      return last_fc_name, new_fc
  
imports: from collections import defaultdict
  from typing import Callable, Dict, List, NamedTuple, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import Linear, Module
  from torch.utils.data import DataLoader, Dataset
  from avalanche.benchmarks import OnlineCLExperience
  from avalanche.models.batch_renorm import BatchRenorm2D
label_desc: Use image node_id 11 for calling a global function with example usage: adapt_classification_layer(model, num_classes, bias) and returns: last_fc_name,  new_fc