class_docstr: Apply statistical operation to a sample (image/ndarray/tensor).
  
  Notes:
      Percentile operation uses a partial function that embeds different kwargs (q).
      In order to print the result nicely, data_addon is added to map the numbers
      generated by percentile to different keys ("percentile_00_5" for example).
      Annotation of the postfix means the percentage for percentile computation.
      For example, _00_5 means 0.5% and _99_5 means 99.5%.
  
  Example:
  
      .. code-block:: python
  
          # use the existing operations
          import numpy as np
          op = SampleOperations()
          data_np = np.random.rand(10, 10).astype(np.float64)
          print(op.evaluate(data_np))
  
          # add a new operation
          op.update({"sum": np.sum})
          print(op.evaluate(data_np))
code: def __init__(self) -> None:
      self.data = {
          "max": max,
          "mean": mean,
          "median": median,
          "min": min,
          "stdev": std,
          "percentile": partial(percentile, q=[0.5, 10, 90, 99.5]),
      }
      self.data_addon = {
          "percentile_00_5": ("percentile", 0),
          "percentile_10_0": ("percentile", 1),
          "percentile_90_0": ("percentile", 2),
          "percentile_99_5": ("percentile", 3),
      }
  
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 SampleOperations object from inherited base classes: Operations with example: obj = SampleOperations()