code: def __getitem__(self, index):
      idx = self.imgidx[index]
      s = self.imgrec.read_idx(idx)
      header, img = mx.recordio.unpack(s)
      img = mx.image.imdecode(img).asnumpy()  # rgb numpy
      y = np.array(header.label, dtype=np.float32).reshape((-1, 3))
      # print('!!!', y.shape)
      assert y.shape[0] == self.num_eye * 2
      eye_l = y[: self.num_eye, :]
      eye_r = y[self.num_eye :, :]
      mean_z_l = np.mean(eye_l[:32, 2])
      mean_z_r = np.mean(eye_r[:32, 2])
      std_z_l = np.max(np.abs(eye_l[:32, 2]))
      std_z_r = np.max(np.abs(eye_r[:32, 2]))
      eye_l[:, 2] -= mean_z_l
      eye_r[:, 2] -= mean_z_r
      eye_l[:, 2] /= std_z_l
      eye_r[:, 2] /= std_z_r
      # print('!!!', np.max(eye_l[:,2]), np.min(eye_l[:,2]))
      y = np.concatenate((eye_l, eye_r), axis=0)
      # y[:,2] /= 100.0
  
      # if self.is_train:
      #    black_edge = np.random.randint(img.shape[1]//3, size=4)
      #    if np.random.random()<0.5:
      #        img[:black_edge[0],:,:] = 0
      #        img[black_edge[1]*-1:,:,:] = 0
      #        img[:,:black_edge[2],:] = 0
      #        img[:,black_edge[3]*-1:,:] = 0
      # label = torch.tensor(y, dtype=torch.float32)
      # label = y
      kps_xy = []
      kps_z = []
      for i in range(y.shape[0]):
          kps_xy.append((y[i][0], y[i][1]))
          kps_z.append(y[i][2])
      if self.transform is not None:
          # sample = self.transform(image=sample)['image']
          # t = self.transform(image=img, keypoints=label, class_labels=self.class_labels, class_sides=self.class_sides)
          t = self.transform(image=img, keypoints=kps_xy)
          flipped = False
          # print(t.keys())
          # print('!!!flipped:', flipped)
          img = t["image"]
          label_xy = t["keypoints"]
          label_xy = np.array(label_xy, dtype=np.float32)
          label_xy /= self.input_size / 2
          label_xy -= 1.0
          label_z = np.array(kps_z, dtype=np.float32).reshape((-1, 1))
          # label_z /= (self.input_size/2)
          label = np.concatenate((label_xy, label_z), axis=1)
          # label = label.flatten()
          label = torch.tensor(label, dtype=torch.float32)
      # print('label:', label.shape)
      return img, label
  
imports: import os
  import os.path
  import queue
  import mxnet
  import pickle
  import threading
  import logging
  import numpy
  import insightface
  from insightface.utils import face_align
  import torch
  from torch.utils.data import DataLoader, Dataset
  from torchvision import transforms
  import cv2
  import albumentations
  from albumentations.pytorch import ToTensorV2
  from .augs import RectangleBorderAugmentation
label_desc: Use image node_id 3 for calling the GazeDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: img,  label