code: def clip_gradients(grads, theta):
      total_grad_norm = jn.linalg.norm(
          jn.array([jn.linalg.norm(g) for g in grads])
      )
      scale_factor = jn.minimum(theta / total_grad_norm, 1.0)
      return [g * scale_factor for g in grads]
  
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 8 for calling a global function with example usage: clip_gradients(grads, theta) and returns: unknown