class_docstr: Apply statistical operation to summarize a dict. The key-value looks like: {"max", "min"
  ,"mean", ....}. The value may contain multiple values in a list format. Then this operation
  will apply the operation to the list. Typically, the dict is generated by multiple
  `SampleOperation` and `concat_multikeys_to_dict` functions.
  
  Examples:
  
      .. code-block:: python
  
          import numpy as np
          data = {
              "min": np.random.rand(4),
              "max": np.random.rand(4),
              "mean": np.random.rand(4),
              "sum": np.random.rand(4),
          }
          op = SummaryOperations()
          print(op.evaluate(data)) # "sum" is not registered yet, so it won't contain "sum"
  
          op.update({"sum", np.sum})
          print(op.evaluate(data)) # output has "sum"
code: def __init__(self) -> None:
      self.data = {
          "max": max,
          "mean": mean,
          "median": mean,
          "min": min,
          "stdev": mean,
          "percentile_00_5": mean,
          "percentile_10_0": mean,
          "percentile_90_0": mean,
          "percentile_99_5": mean,
      }
  
file_path: MONAI/monai/auto3dseg/operations.py
imports: from __future__ import annotations
  from collections import UserDict
  from functools import partial
  from typing import Any
  from monai.transforms.utils_pytorch_numpy_unification import max, mean, median, min, percentile, std
label_desc: Use image node_id 1 to create a new SummaryOperations object from inherited base classes: Operations with example: obj = SummaryOperations()