code: def train_one_batch(self, x, y, dist_option, spars):
      out = self.forward(x)
      loss = self.softmax_cross_entropy(out, y)
  
      if dist_option == "plain":
          self.optimizer(loss)
      elif dist_option == "half":
          self.optimizer.backward_and_update_half(loss)
      elif dist_option == "partialUpdate":
          self.optimizer.backward_and_partial_update(loss)
      elif dist_option == "sparseTopK":
          self.optimizer.backward_and_sparse_update(
              loss, topK=True, spars=spars
          )
      elif dist_option == "sparseThreshold":
          self.optimizer.backward_and_sparse_update(
              loss, topK=False, spars=spars
          )
      return out, loss
  
imports: import json
  import os
  import time
  from glob import glob
  import numpy
  from PIL import Image
  from singa import device, layer, model, opt, tensor
  from tqdm import tqdm
  from transforms import Compose, Normalize, ToTensor
label_desc: Use image node_id 4 for calling the CNNModel obj's underlying member method code with example usage: obj.train_one_batch(x, y, dist_option, spars) and returns: out,  loss