code: def __getitem__(self, index):
      label_class = []
      image_path, label_path = self.data_list[index]
      image = cv2.imread(image_path, cv2.IMREAD_COLOR)
      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
      image = np.float32(image)
      label = cv2.imread(label_path, cv2.IMREAD_GRAYSCALE)
  
      if (
          image.shape[0] != label.shape[0]
          or image.shape[1] != label.shape[1]
      ):
          raise (
              RuntimeError(
                  "Query Image & label shape mismatch: "
                  + image_path
                  + " "
                  + label_path
                  + "\n"
              )
          )
      label_class = np.unique(label).tolist()
      if 0 in label_class:
          label_class.remove(0)
      if 255 in label_class:
          label_class.remove(255)
      new_label_class = []
      for c in label_class:
          if c in self.sub_val_list:
              if self.mode == "val" or self.mode == "test":
                  new_label_class.append(c)
          if c in self.sub_list:
              if self.mode == "train":
                  new_label_class.append(c)
      label_class = new_label_class
      assert len(label_class) > 0
  
      class_chosen = label_class[
          random.randint(1, len(label_class)) - 1
      ]
      class_chosen = class_chosen
      target_pix = np.where(label == class_chosen)
      ignore_pix = np.where(label == 255)
      label[:, :] = 0
      if target_pix[0].shape[0] > 0:
          label[target_pix[0], target_pix[1]] = 1
      label[ignore_pix[0], ignore_pix[1]] = 255
  
      file_class_chosen = self.sub_class_file_list[class_chosen]
      num_file = len(file_class_chosen)
  
      support_image_path_list = []
      support_label_path_list = []
      support_idx_list = []
      for k in range(self.shot):
          support_idx = random.randint(1, num_file) - 1
          support_image_path = image_path
          support_label_path = label_path
          while (
              support_image_path == image_path
              and support_label_path == label_path
          ) or support_idx in support_idx_list:
              support_idx = random.randint(1, num_file) - 1
              (
                  support_image_path,
                  support_label_path,
              ) = file_class_chosen[support_idx]
          support_idx_list.append(support_idx)
          support_image_path_list.append(support_image_path)
          support_label_path_list.append(support_label_path)
  
      support_image_list = []
      support_label_list = []
      subcls_list = []
      for k in range(self.shot):
          if self.mode == "train":
              subcls_list.append(self.sub_list.index(class_chosen))
          else:
              subcls_list.append(self.sub_val_list.index(class_chosen))
          support_image_path = support_image_path_list[k]
          support_label_path = support_label_path_list[k]
          support_image = cv2.imread(
              support_image_path, cv2.IMREAD_COLOR
          )
          support_image = cv2.cvtColor(support_image, cv2.COLOR_BGR2RGB)
          support_image = np.float32(support_image)
          support_label = cv2.imread(
              support_label_path, cv2.IMREAD_GRAYSCALE
          )
          target_pix = np.where(support_label == class_chosen)
          ignore_pix = np.where(support_label == 255)
          support_label[:, :] = 0
          support_label[target_pix[0], target_pix[1]] = 1
          support_label[ignore_pix[0], ignore_pix[1]] = 255
          if (
              support_image.shape[0] != support_label.shape[0]
              or support_image.shape[1] != support_label.shape[1]
          ):
              raise (
                  RuntimeError(
                      "Support Image & label shape mismatch: "
                      + support_image_path
                      + " "
                      + support_label_path
                      + "\n"
                  )
              )
          support_image_list.append(support_image)
          support_label_list.append(support_label)
      assert (
          len(support_label_list) == self.shot
          and len(support_image_list) == self.shot
      )
  
      raw_label = label.copy()
      if self.transform is not None:
          image, label = self.transform(image, label)
          for k in range(self.shot):
              (
                  support_image_list[k],
                  support_label_list[k],
              ) = self.transform(
                  support_image_list[k], support_label_list[k]
              )
  
      s_xs = support_image_list
      s_ys = support_label_list
      s_x = s_xs[0].unsqueeze(0)
      for i in range(1, self.shot):
          s_x = torch.cat([s_xs[i].unsqueeze(0), s_x], 0)
      s_y = s_ys[0].unsqueeze(0)
      for i in range(1, self.shot):
          s_y = torch.cat([s_ys[i].unsqueeze(0), s_y], 0)
  
      if self.mode == "train":
          return image, label, s_x, s_y, subcls_list
      else:
          return image, label, s_x, s_y, subcls_list, raw_label
  
file_path: pfe-net/util/dataset.py
imports: import os
  import os.path
  import cv2
  import numpy
  from torch.utils.data import Dataset
  import torch.nn.functional
  import torch
  import random
  import time
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling the SemData obj's underlying member method code with example usage: obj.__getitem__(index) and returns: image,  label,  s_x,  s_y,  subcls_list, image,  label,  s_x,  s_y,  subcls_list,  raw_label