class_docstr: This is the Spinal Cord Gray Matter Challenge dataset.
  
  :param root_dir: the directory containing the test dataset.
  :param site_ids: a list of site ids to filter (i.e. [1, 3]).
  :param subj_ids: the list of subject ids to filter.
  :param transform: the transformations that should be applied.
  :param cache: if the data should be cached in memory or not.
  :param slice_axis: axis to make the slicing (default axial).
  
  .. note:: This dataset assumes that you only have one class in your
            ground truth mask (w/ 0's and 1's). It also doesn't
            automatically resample the dataset.
  
  .. seealso::
      Prados, F., et al (2017). Spinal cord grey matter
      segmentation challenge. NeuroImage, 152, 312–329.
      https://doi.org/10.1016/j.neuroimage.2017.03.010
  
      Challenge Website:
      http://cmictig.cs.ucl.ac.uk/spinal-cord-grey-matter-segmentation-challenge
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code: def _build_train_input_filename(site_id, subj_id, rater_id=None):
      if rater_id is None:
          return "site{:d}-sc{:02d}-image.nii.gz".format(
              site_id, subj_id
          )
      else:
          return "site{:d}-sc{:02d}-mask-r{:d}.nii.gz".format(
              site_id, subj_id, rater_id
          )
  
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 2 for calling the SCGMChallenge2DTest obj's underlying member method code with example usage: obj._build_train_input_filename(site_id, subj_id, rater_id) and returns: str, str