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 __init__(self, *datasets):
      self._datasets = datasets
  
imports: from chainer.dataset import dataset_mixin
label_desc: Use image node_id 1 to create a new ConcatenatedDataset object from inherited base classes: dataset_mixin with example: obj = ConcatenatedDataset()