code: def _train_op_fn(
      loss,
      optimizer_fn,
      l2_regularization=-1,
      gradient_max_norm=-1,
      use_synchronous_optimizer=False,
  ):
      """Returns the op to optimize the loss.
  
      Supports l2 regularization, learning rate decay and gradient clipping.
  
      Args:
        loss: The training loss before regularization.
        optimizer_fn: the optimization function.
        l2_regularization: a float that will multiply the l2 weight norms in the
          loss function.
        gradient_max_norm: a float - maximal gradient update allowed.
        use_synchronous_optimizer: a bool whether to use synchronous optimization.
  
      Returns:
        `ModelSpec` with logits, loss, train_ops and train_hooks.
      """
      total_loss = loss
      if l2_regularization > 0:
          weight_losses = [
              tf.multiply(
                  tf.nn.l2_loss(weight),
                  l2_regularization,
                  name="l2_weight_loss",
              )
              for weight in tf.compat.v1.trainable_variables()
          ]
          total_loss = tf.add_n(
              weight_losses + [loss], name="total_loss"
          )
  
      global_step = tf.compat.v1.train.get_or_create_global_step()
  
      opt = optimizer_fn()
  
      train_hooks = []
      if use_synchronous_optimizer:
          config = tf.estimator.RunConfig()
          workers = config.num_worker_replicas + 1
          tolerance = _compute_tolerance(workers)
          to_aggregate = workers - tolerance
          opt = tf.compat.v1.train.SyncReplicasOptimizer(
              opt,
              replicas_to_aggregate=to_aggregate,
              total_num_replicas=workers,
          )
          sync_replicas_hook = opt.make_session_run_hook(
              config.is_chief
          )
          train_hooks.append(sync_replicas_hook)
  
      tvars = tf.compat.v1.trainable_variables()
      grads_and_vars = opt.compute_gradients(
          loss=total_loss, var_list=tvars
      )
      # TODO(b/172564129): switch to tf.contrib.estimator.clip_gradients_by_norm
      if gradient_max_norm > 0.0:
          grads = [gv[0] for gv in grads_and_vars]
          tvars = [gv[1] for gv in grads_and_vars]
          grads, _ = tf.clip_by_global_norm(grads, gradient_max_norm)
          grads_and_vars = list(zip(grads, tvars))
  
      if use_synchronous_optimizer:
          apply_gradients_op = opt.apply_gradients(
              grads_and_vars, global_step
          )
      else:
          apply_gradients_op = opt.apply_gradients(grads_and_vars)
  
      update_ops = tf.compat.v1.get_collection(
          tf.compat.v1.GraphKeys.UPDATE_OPS
      )
      with tf.control_dependencies(update_ops):
          return tf.group(apply_gradients_op), train_hooks
  
imports: import collections
  import functools
  import inspect
  from absl import logging
  from model_search import block_builder
  from model_search import ensembler
  from model_search.architecture import architecture_utils
  from model_search.architecture import tower
  from model_search.ensembler import EnsembleLogits
  from model_search.generators import base_tower_generator
  from model_search.generators import trial_utils
  from model_search.meta import distillation
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 5 for calling a global function with example usage: _train_op_fn(loss, optimizer_fn, l2_regularization, gradient_max_norm, use_synchronous_optimizer) and returns: tf,  train_hooks