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: Args:
      stats (List[Stat]):
          The list of statistics you wish to track
docstr_tok: ["Args",":","stats","(","List","[","Stat","]",")",":","The","list","of","statistics","you","wish","to","track"]
code: def __init__(self, stats: List[Stat]) -> None:
      r"""
      Args:
          stats (List[Stat]):
              The list of statistics you wish to track
      """
      self._summarizers: List[SummarizerSingleTensor] = []
      self._is_inputs_tuple: Optional[bool] = None
      self._stats, self._summary_stats_indicies = _reorder_stats(stats)
  
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 1 to create a new Summarizer object  with example: obj = Summarizer(stats)