class_docstr: Dataset which concatenates some base datasets.
  
  This dataset wraps some base datasets and works as a concatenated dataset.
  For example, if a base dataset with 10 samples and
  another base dataset with 20 samples are given, this dataset works as
  a dataset which has 30 samples.
  
  Args:
      datasets: The underlying datasets. Each dataset has to support
          :meth:`__len__` and :meth:`__getitem__`.
class_docstr_tok: ["Dataset","which","concatenates","some","base","datasets",".","This","dataset","wraps","some","base","datasets","and","works","as","a","concatenated","dataset",".","For","example",",","if","a","base","dataset","with","10","samples","and","another","base","dataset","with","20","samples","are","given",",","this","dataset","works","as","a","dataset","which","has","30","samples",".","Args",":","datasets",":","The","underlying","datasets",".","Each","dataset","has","to","support",":","meth",":","`","__len__","`","and",":","meth",":","`","__getitem__","`","."]
code: def __len__(self):
      return sum(len(dataset) for dataset in self._datasets)
  
imports: from chainer.dataset import dataset_mixin
label_desc: Use image node_id 2 for calling the ConcatenatedDataset obj's underlying member method code with example usage: obj.__len__() and returns: sum