code: def train_op(x, xl):
      g, v = gv(x, xl)  # returns gradients, loss
      clipped_g = clip_gradients(g, theta)
      opt(lr, clipped_g)
      model_ema.update_ema()
      return v
  
imports: import collections
  import random
  import re
  import jax
  import jax.numpy
  import tensorflow_datasets
  import objax
  from objax.functional import one_hot
  from objax.zoo.rnn import RNN
label_desc: Use image node_id 9 for calling a global function with example usage: train_op(x, xl) and returns: v