docstr: One step for training, which should be called as forward computation.
  
  Args:
      batch(list[paddle.Tensor]) : The one batch data, which contains images and labels.
      batch_idx(int) : The index of batch.
  
  Returns:
      results(dict) : The model outputs, such as loss and metrics.
code: def training_step(self, batch: int, batch_idx: int) -> dict:
      """
      One step for training, which should be called as forward computation.
  
      Args:
          batch(list[paddle.Tensor]) : The one batch data, which contains images and labels.
          batch_idx(int) : The index of batch.
  
      Returns:
          results(dict) : The model outputs, such as loss and metrics.
      """
      return self.validation_step(batch, batch_idx)
  
imports: import time
  import os
  import base64
  import argparse
  from typing import List, Union, Tuple
  from collections import OrderedDict
  import cv2
  import paddle
  import numpy
  import paddle.nn
  import paddle.nn.functional
  from PIL import Image
  import paddlehub.vision.transforms
  import paddlehub.vision.functional
  from paddlehub.vision import utils
  from paddlehub.module.module import serving, RunModule, runnable
  from paddlehub.utils.utils import base64_to_cv2, cv2_to_base64, Version
label_desc: Use image node_id 1 for calling the ImageClassifierModule obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: self