code: def flatgrad(loss, var_list, clip_norm=None):
      grads = tf.gradients(loss, var_list)
      if clip_norm is not None:
          grads = [
              tf.clip_by_norm(grad, clip_norm=clip_norm)
              for grad in grads
          ]
      return tf.concat(
          axis=0,
          values=[
              tf.reshape(
                  grad if grad is not None else tf.zeros_like(v),
                  [numel(v)],
              )
              for (v, grad) in zip(var_list, grads)
          ],
      )
  
imports: import numpy
  import tensorflow
  import copy
  import os
  import functools
  import collections
  import multiprocessing
label_desc: Use image node_id 15 for calling a global function with example usage: flatgrad(loss, var_list, clip_norm) and returns: tf