class_docstr: group of user specified constraints for choosing window samples
class_docstr_tok: ["group","of","user","specified","constraints","for","choosing","window","samples"]
docstr: number of voxels from ratio
  :param win_size:
  :return:
docstr_tok: ["number","of","voxels","from","ratio",":","param","win_size",":",":","return",":"]
code: def min_number_from_ratio(self, win_size):
      """
      number of voxels from ratio
      :param win_size:
      :return:
      """
      num_elements = np.prod(win_size)
      print(num_elements, self.min_ratio)
      min_num = np.ceil(self.min_ratio * num_elements)
      return min_num
  
file_path: NiftyNet/niftynet/contrib/segmentation_selective_sampler/sampler_selective.py
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import tensorflow
  from scipy import ndimage
  from scipy.signal import fftconvolve
  from niftynet.engine.image_window import N_SPATIAL
  from niftynet.engine.sampler_uniform_v2 import UniformSampler
label_desc: Use image node_id 2 for calling the Constraint obj's underlying member method code with example usage: obj.min_number_from_ratio(win_size) and returns: min_num