class_docstr: This class simply wraps over a given a set of SummarizerSingleTensor's in order
  to summarise multiple input tensors.
  
  Basic usage:
  
  >>>from captum.attr.aggregator import Summarizer
  >>>from captum.attr._utils.stats import Mean, StdDev
  >>>
  >>>attrib = torch.tensor([1, 2, 3, 4, 5])
  >>>
  >>>summ = Summarizer([Mean(), StdDev(0])
  >>>summ.update(attrib)
  >>>
  >>>print(summ.summary['mean'])
docstr: Effectively calls `get` on each `Stat` object within this object for each input
  
  Returns:
      A dict or list of dict: mapping from the Stat
      object's `name` to the associated value of `get`
docstr_tok: ["Effectively","calls","`","get","`","on","each","`","Stat","`","object","within","this","object","for","each","input","Returns",":","A","dict","or","list","of","dict",":","mapping","from","the","Stat","object","'s","`","name","`","to","the","associated","value","of","`","get","`"]
code: def summary(
      self,
  ) -> Optional[
      Union[
          Dict[str, Optional[Tensor]], List[Dict[str, Optional[Tensor]]]
      ]
  ]:
      r"""
      Effectively calls `get` on each `Stat` object within this object for each input
  
      Returns:
          A dict or list of dict: mapping from the Stat
          object's `name` to the associated value of `get`
      """
      if len(self._summarizers) == 0:
          return None
  
      temp = [summ.summary for summ in self._summarizers]
      return temp if self._is_inputs_tuple else temp[0]
  
file_path: captum/captum/attr/_utils/summarizer.py
imports: from typing import Dict, List, Optional, Tuple, Type, Union
  import torch
  from captum.attr._utils.stat import Count, Max, Mean, Min, MSE, Stat, StdDev, Sum, Var
  from captum.log import log_usage
  from torch import Tensor
label_desc: Use image node_id 4 for calling the Summarizer obj's underlying member method code with example usage: obj.summary() and returns: unknown, None