code: def create_train_op(
      total_loss,
      optimizer,
      global_step=_USE_GLOBAL_STEP,
      update_ops=None,
      variables_to_train=None,
      transform_grads_fn=None,
      summarize_gradients=False,
      gate_gradients=tf.compat.v1.train.Optimizer.GATE_OP,
      aggregation_method=None,
      check_numerics=True,
  ):
      """Creates an `Operation` that evaluates the gradients and returns the loss.
  
      Args:
        total_loss: A `Tensor` representing the total loss.
        optimizer: A tf.Optimizer to use for computing the gradients.
        global_step: A `Tensor` representing the global step variable. If left as
          `_USE_GLOBAL_STEP`, then tf.train.get_or_create_global_step() is used.
        update_ops: An optional list of updates to execute. If `update_ops` is
          `None`, then the update ops are set to the contents of the
          `tf.GraphKeys.UPDATE_OPS` collection. If `update_ops` is not `None`, but
          it doesn't contain all of the update ops in `tf.GraphKeys.UPDATE_OPS`, a
          warning will be displayed.
        variables_to_train: an optional list of variables to train. If None, it will
          default to all tf.trainable_variables().
        transform_grads_fn: A function which takes a single argument, a list of
          gradient to variable pairs (tuples), performs any requested gradient
          updates, such as gradient clipping or multipliers, and returns the updated
          list.
        summarize_gradients: Whether or not add summaries for each gradient.
        gate_gradients: How to gate the computation of gradients. See tf.Optimizer.
        aggregation_method: Specifies the method used to combine gradient terms.
          Valid values are defined in the class `AggregationMethod`.
        check_numerics: Whether or not we apply check_numerics.
  
      Returns:
        A `Tensor` that when evaluated, computes the gradients and returns the total
          loss value.
      """
      if global_step is _USE_GLOBAL_STEP:
          global_step = tf.compat.v1.train.get_or_create_global_step()
  
      # Update ops use GraphKeys.UPDATE_OPS collection if update_ops is None.
      global_update_ops = set(
          tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS)
      )
      if update_ops is None:
          update_ops = global_update_ops
      else:
          update_ops = set(update_ops)
      if not global_update_ops.issubset(update_ops):
          tf.compat.v1.logging.warning(
              "update_ops in create_train_op does not contain all the "
              "update_ops in GraphKeys.UPDATE_OPS"
          )
  
      # Make sure update_ops are computed before total_loss.
      if update_ops:
          with tf.control_dependencies(update_ops):
              barrier = tf.no_op(name="update_barrier")
          with tf.control_dependencies([barrier]):
              total_loss = tf.identity(total_loss)
  
      if variables_to_train is None:
          # Default to tf.trainable_variables()
          variables_to_train = tf.compat.v1.trainable_variables()
      else:
          # Make sure that variables_to_train are in tf.trainable_variables()
          trainable_variables = tf.compat.v1.trainable_variables()
          for v in variables_to_train:
              assert v.trainable or v in trainable_variables
  
      assert variables_to_train
  
      # Create the gradients. Note that apply_gradients adds the gradient
      # computation to the current graph.
      grads = optimizer.compute_gradients(
          total_loss,
          variables_to_train,
          gate_gradients=gate_gradients,
          aggregation_method=aggregation_method,
      )
  
      if transform_grads_fn:
          grads = transform_grads_fn(grads)
  
      # Summarize gradients.
      if summarize_gradients:
          with tf.name_scope("summarize_grads"):
              add_gradients_summaries(grads, global_step)
  
      # Create gradient updates.
      grad_updates = optimizer.apply_gradients(
          grads, global_step=global_step
      )
  
      with tf.name_scope("train_op"):
          # Make sure total_loss is valid.
          if check_numerics:
              total_loss = tf.debugging.check_numerics(
                  total_loss, "LossTensor is inf or nan"
              )
  
          # Ensure the train_tensor computes grad_updates.
          with tf.control_dependencies([grad_updates]):
              train_op = tf.identity(total_loss, name="train_op")
  
      return train_op
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import functools
  import inspect
  from absl import logging
  import numpy
  import six
  import tensorflow
  from tf_agents.utils import common
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 9 for calling a global function with example usage: create_train_op(total_loss, optimizer, global_step, update_ops, variables_to_train, transform_grads_fn, summarize_gradients, gate_gradients, aggregation_method, check_numerics) and returns: train_op