class_docstr: Iterate over datasets and provide a batch per dataset in each mini-batch.
class_docstr_tok: ["Iterate","over","datasets","and","provide","a","batch","per","dataset","in","each","mini-batch","."]
code: def __len__(self):
      return self.termination_dataset_iterations
  
imports: from typing import Any, Dict, Iterator, List, Optional, Sequence, Sized, Union
  import numpy
  from torch.utils.data import DistributedSampler, Dataset
  from torch.utils.data.dataloader import DataLoader
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.data_attribute import DataAttribute
  from avalanche.benchmarks.utils.ffcv_support.ffcv_components import HybridFfcvLoader, has_ffcv_support
  from avalanche.distributed.distributed_helper import DistributedHelper
  from torch.utils.data.sampler import Sampler, BatchSampler
  from torch.utils.data import ConcatDataset
label_desc: Use image node_id 2 for calling the MultiDatasetSampler obj's underlying member method code with example usage: obj.__len__() and returns: self