class_docstr: This is a generic class for 2D (slice-wise) segmentation datasets.
  
  :param filename_pairs: a list of tuples in the format (input filename,
                         ground truth filename).
  :param slice_axis: axis to make the slicing (default axial).
  :param cache: if the data should be cached in memory or not.
  :param transform: transformations to apply.
class_docstr_tok: ["This","is","a","generic","class","for","2D","(","slice-wise",")","segmentation","datasets",".",":","param","filename_pairs",":","a","list","of","tuples","in","the","format","(","input","filename",",","ground","truth","filename",")",".",":","param","slice_axis",":","axis","to","make","the","slicing","(","default","axial",")",".",":","param","cache",":","if","the","data","should","be","cached","in","memory","or","not",".",":","param","transform",":","transformations","to","apply","."]
docstr: This method will replace the current transformation for the
  dataset.
  
  :param transform: the new transformation
docstr_tok: ["This","method","will","replace","the","current","transformation","for","the","dataset",".",":","param","transform",":","the","new","transformation"]
code: def set_transform(self, transform):
      """This method will replace the current transformation for the
      dataset.
  
      :param transform: the new transformation
      """
      self.transform = transform
  
imports: import os
  import re
  import collections
  from medicaltorch import transforms
  from tqdm import tqdm
  import numpy
  import nibabel
  from torch.utils.data import Dataset
  import torch
  from torch._six import string_classes, int_classes
  from PIL import Image
label_desc: Use image node_id 4 for calling the MRI2DSegmentationDataset obj's underlying member method code with example usage: obj.set_transform(transform) without return types