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"
docstr: Applies the callables to the data, and convert the numerics to list or Python
  numeric types (int/float).
  
  Args:
      data: input data
docstr_tok: ["Applies","the","callables","to","the","data",",","and","convert","the","numerics","to","list","or","Python","numeric","types","(","int\/float",")",".","Args",":","data",":","input","data"]
code: def evaluate(self, data: Any, **kwargs: Any) -> dict:
      """
      Applies the callables to the data, and convert the numerics to list or Python
      numeric types (int/float).
  
      Args:
          data: input data
      """
      return {
          k: v(data[k], **kwargs).tolist()
          for k, v in self.data.items()
          if (callable(v) and k in data)
      }
  
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 2 for calling the SummaryOperations obj's underlying member method code with example usage: obj.evaluate(data) and returns: unknown