class_docstr: Base class for input data adapter.
  In order to simplify the training code path, all the input data
  object will be converted to a `generator` if possible.
  The sample usage of this class is like:
  
  ```
  x = list(range(100))
  adapter_cls = [ArrayDataAdapter, ..., ListsOfScalarsDataAdapter]
  applicable_adapters = [cls for cls in adapter_cls if cls.can_handle(x)]
  if len(applicable_adapters) != 1:
    raise ValueError("Expect only one adapter class to handle the input")
  dataset = applicable_adapters[0](x).get_dataset()
  for data in dataset():
    # training
  ```
docstr: Return a representative size for batches in the dataset.
  This is not guaranteed to be the batch size for all batches in the
  dataset. It just needs to be a rough approximation for batch sizes in
  the dataset.
  Returns:
    int, a representative size for batches found in the dataset,
    or None if it is unknown.
docstr_tok: ["Return","a","representative","size","for","batches","in","the","dataset",".","This","is","not","guaranteed","to","be","the","batch","size","for","all","batches","in","the","dataset",".","It","just","needs","to","be","a","rough","approximation","for","batch","sizes","in","the","dataset",".","Returns",":","int",",","a","representative","size","for","batches","found","in","the","dataset",",","or","None","if","it","is","unknown","."]
code: def representative_batch_size(self):
      """Return a representative size for batches in the dataset.
      This is not guaranteed to be the batch size for all batches in the
      dataset. It just needs to be a rough approximation for batch sizes in
      the dataset.
      Returns:
        int, a representative size for batches found in the dataset,
        or None if it is unknown.
      """
      return self.batch_size()
  
file_path: elegy/elegy/data/data_adapter.py
imports: import abc
  import typing
  import six
label_desc: Use image node_id 6 for calling the DataAdapter obj's underlying member method code with example usage: obj.representative_batch_size() and returns: self