code: def __getitem__(self, index):
      hyp = self.hyp
      if self.mosaic:
          # load mosaic
          img, labels = load_mosaic(self, index)
          shapes = None
      else:
          # load image
          img, (h0, w0), (h, w) = load_image(self, index)
  
          # letterbox
          shape = (
              self.batch_shapes[self.batch[index]]
              if self.rect
              else self.img_size
          )  # final letterboxed shape
          img, ratio, pad = letterbox(
              img, shape, auto=False, scale_up=self.augment
          )
          shapes = (h0, w0), (
              (h / h0, w / w0),
              pad,
          )  # for COCO mAP rescaling
  
          # load labels
          labels = []
          x = self.labels[index]
          if x.size > 0:
              # Normalized xywh to pixel xyxy format
              labels = x.copy()  # label: class, x, y, w, h
              labels[:, 1] = (
                  ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0]
              )  # pad width
              labels[:, 2] = (
                  ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1]
              )  # pad height
              labels[:, 3] = (
                  ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
              )
              labels[:, 4] = (
                  ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
              )
  
      if self.augment:
          # Augment imagespace
          if not self.mosaic:
              img, labels = random_affine(
                  img,
                  labels,
                  degrees=hyp["degrees"],
                  translate=hyp["translate"],
                  scale=hyp["scale"],
                  shear=hyp["shear"],
              )
  
          # Augment colorspace
          augment_hsv(
              img,
              h_gain=hyp["hsv_h"],
              s_gain=hyp["hsv_s"],
              v_gain=hyp["hsv_v"],
          )
  
      nL = len(labels)  # number of labels
      if nL:
          # convert xyxy to xywh
          labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])
  
          # Normalize coordinates 0-1
          labels[:, [2, 4]] /= img.shape[0]  # height
          labels[:, [1, 3]] /= img.shape[1]  # width
  
      if self.augment:
          # random left-right flip
          lr_flip = True  # 随机水平翻转
          if lr_flip and random.random() < 0.5:
              img = np.fliplr(img)
              if nL:
                  labels[:, 1] = 1 - labels[:, 1]  # 1 - x_center
  
          # random up-down flip
          ud_flip = False
          if ud_flip and random.random() < 0.5:
              img = np.flipud(img)
              if nL:
                  labels[:, 2] = 1 - labels[:, 2]  # 1 - y_center
  
      labels_out = torch.zeros((nL, 6))  # nL: number of labels
      if nL:
          labels_out[:, 1:] = torch.from_numpy(labels)
  
      # Convert BGR to RGB, and HWC to CHW(3x512x512)
      img = img[:, :, ::-1].transpose(2, 0, 1)
      img = np.ascontiguousarray(img)
  
      return (
          torch.from_numpy(img),
          labels_out,
          self.img_files[index],
          shapes,
          index,
      )
  
imports: import math
  import os
  import random
  import shutil
  from pathlib import Path
  import cv2
  import numpy
  import torch
  from PIL import Image, ExifTags
  from torch.utils.data import Dataset
  from tqdm import tqdm
  from build_utils.utils import xyxy2xywh, xywh2xyxy
label_desc: Use image node_id 3 for calling the LoadImagesAndLabels obj's underlying member method code with example usage: obj.__getitem__(index) and returns: torch,  labels_out,  self,  shapes,  index