class_docstr: Bias Correction (BiC) plugin.
  
  Technique introduced in:
  "Wu, Yue, et al. "Large scale incremental learning." Proceedings
  of the IEEE/CVF Conference on Computer Vision and Pattern
  Recognition. 2019"
  
  Implementation based on FACIL, as in:
  https://github.com/mmasana/FACIL/blob/master/src/approach/bic.py
code: def before_training(
      self, strategy: "SupervisedTemplate", *args, **kwargs
  ):
      assert not isinstance(
          strategy.model, MultiTaskModule
      ), "BiC only supported for Class Incremetnal Learning (single head)"
  
imports: from collections import defaultdict
  from typing import Dict, List, Optional, TYPE_CHECKING, Sequence, Set, SupportsInt
  from copy import deepcopy
  import torch
  from torch.utils.data import DataLoader
  from torch.optim.lr_scheduler import MultiStepLR
  from avalanche.benchmarks.utils import _taskaware_classification_subset, _concat_taskaware_classification_datasets
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.data_loader import ReplayDataLoader
  from avalanche.training.plugins.strategy_plugin import SupervisedPlugin
  from avalanche.training.storage_policy import ExemplarsBuffer, ExperienceBalancedBuffer, ReservoirSamplingBuffer
  from avalanche.models.dynamic_modules import MultiTaskModule
  from avalanche.models.bic_model import BiasLayer
label_desc: Use image node_id 2 for calling the BiCPlugin obj's underlying member method code with example usage: obj.before_training(strategy) without return types