class_docstr: A basic class for under-sampling mask setup. It provides common
  features for under-sampling mask generators.
  For example, RandomMaskFunc and EquispacedMaskFunc (two mask
  transform objects defined right after this module)
  both inherit MaskFunc to properly setup properties like the
  acceleration factor.
docstr: If multiple values are provided for center_fractions and
  accelerations, this function selects one value uniformly
  for each training/test sample.
  
  Returns:
      A tuple containing
          (1) center_fraction: chosen fraction of center kspace
          lines to exclude from under-sampling
          (2) acceleration: chosen acceleration factor
docstr_tok: ["If","multiple","values","are","provided","for","center_fractions","and","accelerations",",","this","function","selects","one","value","uniformly","for","each","training\/test","sample",".","Returns",":","A","tuple","containing","(","1",")","center_fraction",":","chosen","fraction","of","center","kspace","lines","to","exclude","from","under-sampling","(","2",")","acceleration",":","chosen","acceleration","factor"]
code: def randomize_choose_acceleration(self) -> Sequence[float]:
      """
      If multiple values are provided for center_fractions and
      accelerations, this function selects one value uniformly
      for each training/test sample.
  
      Returns:
          A tuple containing
              (1) center_fraction: chosen fraction of center kspace
              lines to exclude from under-sampling
              (2) acceleration: chosen acceleration factor
      """
      choice = self.R.randint(0, len(self.accelerations))
      center_fraction = self.center_fractions[choice]
      acceleration = self.accelerations[choice]
      return center_fraction, acceleration
  
imports: from __future__ import annotations
  from abc import abstractmethod
  from collections.abc import Sequence
  import numpy
  from torch import Tensor
  from monai.apps.reconstruction.complex_utils import complex_abs, convert_to_tensor_complex
  from monai.apps.reconstruction.mri_utils import root_sum_of_squares
  from monai.config.type_definitions import NdarrayOrTensor
  from monai.data.fft_utils import ifftn_centered
  from monai.transforms.transform import RandomizableTransform
  from monai.utils.enums import TransformBackends
  from monai.utils.type_conversion import convert_to_tensor
label_desc: Use image node_id 3 for calling the KspaceMask obj's underlying member method code with example usage: obj.randomize_choose_acceleration() and returns: center_fraction,  acceleration