class_docstr: Cumulative training strategy.
  
  At each experience, train model with data from all previous experiences
      and current experience.
docstr: Concatenates all the previous experiences.
docstr_tok: ["Concatenates","all","the","previous","experiences","."]
code: def train_dataset_adaptation(self, **kwargs):
      """
      Concatenates all the previous experiences.
      """
      exp = self.experience
      assert exp is not None
      if self.dataset is None:
          self.dataset = exp.dataset
      else:
          self.dataset = concat_datasets([self.dataset, exp.dataset])
      self.adapted_dataset = self.dataset
  
file_path: avalanche/avalanche/training/supervised/cumulative.py
imports: from typing import Callable, Optional, List, Union
  import torch
  from torch.nn import Module
  from torch.optim import Optimizer
  from torch.utils.data import ConcatDataset
  from avalanche.benchmarks.utils import _concat_taskaware_classification_datasets
  from avalanche.benchmarks.utils.utils import concat_datasets
  from avalanche.training.plugins.evaluation import default_evaluator
  from avalanche.training.plugins import SupervisedPlugin, EvaluationPlugin
  from avalanche.training.templates import SupervisedTemplate
label_desc: Use image node_id 2 for calling the Cumulative obj's underlying member method code with example usage: obj.train_dataset_adaptation() without return types