class_docstr: This class is used to build 2D segmentation datasets. It represents
  a pair of of two data volumes (the input data and the ground truth data).
  
  :param input_filename: the input filename (supported by nibabel).
  :param gt_filename: the ground-truth filename.
  :param cache: if the data should be cached in memory or not.
  :param canonical: canonical reordering of the volume axes.
class_docstr_tok: ["This","class","is","used","to","build","2D","segmentation","datasets",".","It","represents","a","pair","of","of","two","data","volumes","(","the","input","data","and","the","ground","truth","data",")",".",":","param","input_filename",":","the","input","filename","(","supported","by","nibabel",")",".",":","param","gt_filename",":","the","ground-truth","filename",".",":","param","cache",":","if","the","data","should","be","cached","in","memory","or","not",".",":","param","canonical",":","canonical","reordering","of","the","volume","axes","."]
docstr: Return the specified slice from (input, ground truth).
  
  :param slice_index: the slice number.
  :param slice_axis: axis to make the slicing.
docstr_tok: ["Return","the","specified","slice","from","(","input",",","ground","truth",")",".",":","param","slice_index",":","the","slice","number",".",":","param","slice_axis",":","axis","to","make","the","slicing","."]
code: def get_pair_slice(self, slice_index, slice_axis=2):
      """Return the specified slice from (input, ground truth).
  
      :param slice_index: the slice number.
      :param slice_axis: axis to make the slicing.
      """
      if self.cache:
          input_dataobj, gt_dataobj = self.get_pair_data()
      else:
          # use dataobj to avoid caching
          input_dataobj = self.input_handle.dataobj
  
          if self.gt_handle is None:
              gt_dataobj = None
          else:
              gt_dataobj = self.gt_handle.dataobj
  
      if slice_axis not in [0, 1, 2]:
          raise RuntimeError("Invalid axis, must be between 0 and 2.")
  
      if slice_axis == 2:
          input_slice = np.asarray(
              input_dataobj[..., slice_index], dtype=np.float32
          )
      elif slice_axis == 1:
          input_slice = np.asarray(
              input_dataobj[:, slice_index, ...], dtype=np.float32
          )
      elif slice_axis == 0:
          input_slice = np.asarray(
              input_dataobj[slice_index, ...], dtype=np.float32
          )
  
      # Handle the case for unlabeled data
      gt_meta_dict = None
      if self.gt_handle is None:
          gt_slice = None
      else:
          if slice_axis == 2:
              gt_slice = np.asarray(
                  gt_dataobj[..., slice_index], dtype=np.float32
              )
          elif slice_axis == 1:
              gt_slice = np.asarray(
                  gt_dataobj[:, slice_index, ...], dtype=np.float32
              )
          elif slice_axis == 0:
              gt_slice = np.asarray(
                  gt_dataobj[slice_index, ...], dtype=np.float32
              )
  
          gt_meta_dict = SampleMetadata(
              {
                  "zooms": self.gt_handle.header.get_zooms()[:2],
                  "data_shape": self.gt_handle.header.get_data_shape()[
                      :2
                  ],
              }
          )
  
      input_meta_dict = SampleMetadata(
          {
              "zooms": self.input_handle.header.get_zooms()[:2],
              "data_shape": self.input_handle.header.get_data_shape()[
                  :2
              ],
          }
      )
  
      dreturn = {
          "input": input_slice,
          "gt": gt_slice,
          "input_metadata": input_meta_dict,
          "gt_metadata": gt_meta_dict,
      }
  
      return dreturn
  
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 SegmentationPair2D obj's underlying member method code with example usage: obj.get_pair_slice(slice_index, slice_axis) and returns: dreturn