class_docstr: PicReader
class_docstr_tok: ["PicReader"]
docstr: Args:
      record:
  
  Returns:
docstr_tok: ["Args",":","record",":","Returns",":"]
code: def get(self, record: PicRecord) -> tuple:
      """
  
      Args:
          record:
  
      Returns:
  
      """
      relative_path = record.sub_path
      if self.root_path is not None:
          image_path = os.path.join(self.root_path, relative_path)
      else:
          image_path = relative_path
  
      img = self._load_image(image_path)
      # print("org img sum:{}".format(np.sum(np.asarray(img))))
  
      process_data = self.transform(img)
  
      if self.train_type == "single_label":
          if self.class_to_id_dict:
              label = self.class_to_id_dict[record.label]
          else:
              label = int(record.label)
      elif self.train_type == "multi_labels":
          label_tensor = np.zeros((1, self.class_num))
          for label in record.label.split(","):
              label_tensor[0, int(label)] = 1
          label_tensor = np.squeeze(label_tensor)
          label = label_tensor
  
      if self.meta:
          return process_data, label, relative_path
      else:
          return process_data, label
  
imports: import re
  import numpy
  from typing import Dict, List, Optional, Union, Tuple
  import six
  import cv2
  import os
  import paddle
  import paddlehub.vision.transforms
  from PIL import ImageFile
  from auto_augment.autoaug.transform.autoaug_transform import AutoAugTransform
label_desc: Use image node_id 6 for calling the PicReader obj's underlying member method code with example usage: obj.get(record) and returns: process_data,  label,  relative_path, process_data,  label