code: def create_train_step(
      loss,
      optimizer,
      global_step=_USE_GLOBAL_STEP,
      total_loss_fn=None,
      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 a train_step that evaluates the gradients and returns the loss.
  
      Args:
        loss: A (possibly nested tuple of) `Tensor` or function representing the
          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.
        total_loss_fn: Function to call on loss value to access the final item to
          minimize.
        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:
        In graph mode: A (possibly nested tuple of) `Tensor` that when evaluated,
          calculates the current loss, computes the gradients, applies the
          optimizer, and returns the current loss.
        In eager mode: A lambda function that when is called, calculates the loss,
          then computes and applies the gradients and returns the original
          loss values.
      Raises:
        ValueError: if loss is not callable.
        RuntimeError: if resource variables are not enabled.
      """
      if total_loss_fn is None:
          total_loss_fn = lambda x: x
      if not callable(total_loss_fn):
          raise ValueError("`total_loss_fn` should be a function.")
      if not common.resource_variables_enabled():
          raise RuntimeError(common.MISSING_RESOURCE_VARIABLES_ERROR)
      if not tf.executing_eagerly():
          if callable(loss):
              loss = loss()
          if callable(variables_to_train):
              variables_to_train = variables_to_train()
          # Calculate loss first, then calculate train op, then return the original
          # loss conditioned on executing the train op.
          with tf.control_dependencies(tf.nest.flatten(loss)):
              loss = tf.nest.map_structure(
                  lambda t: tf.identity(t, "loss_pre_train"), loss
              )
          train_op = create_train_op(
              total_loss_fn(loss),
              optimizer,
              global_step=global_step,
              update_ops=update_ops,
              variables_to_train=variables_to_train,
              transform_grads_fn=transform_grads_fn,
              summarize_gradients=summarize_gradients,
              gate_gradients=gate_gradients,
              aggregation_method=aggregation_method,
              check_numerics=check_numerics,
          )
  
          with tf.control_dependencies([train_op]):
              return tf.nest.map_structure(
                  lambda t: tf.identity(t, "loss_post_train"), loss
              )
  
      if global_step is _USE_GLOBAL_STEP:
          global_step = tf.compat.v1.train.get_or_create_global_step()
  
      if not callable(loss):
          raise ValueError("`loss` should be a function in eager mode.")
  
      if not isinstance(loss, Future):
          logging.warning(
              "loss should be an instance of eager_utils.Future"
          )
  
      with tf.GradientTape() as tape:
          loss_value = loss()
          total_loss_value = total_loss_fn(loss_value)
      if variables_to_train is None:
          variables_to_train = tape.watched_variables()
      elif callable(variables_to_train):
          variables_to_train = variables_to_train()
      variables_to_train = tf.nest.flatten(variables_to_train)
      grads = tape.gradient(total_loss_value, variables_to_train)
      grads_and_vars = list(zip(grads, variables_to_train))
  
      if transform_grads_fn:
          grads_and_vars = transform_grads_fn(grads_and_vars)
  
      if summarize_gradients:
          with tf.name_scope("summarize_grads"):
              add_gradients_summaries(grads_and_vars, global_step)
  
      if check_numerics:
          with tf.name_scope("train_op"):
              tf.debugging.check_numerics(
                  total_loss_value, "Loss is inf or nan"
              )
  
      optimizer.apply_gradients(grads_and_vars, global_step=global_step)
  
      return loss_value
  
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 8 for calling a global function with example usage: create_train_step(loss, optimizer, global_step, total_loss_fn, update_ops, variables_to_train, transform_grads_fn, summarize_gradients, gate_gradients, aggregation_method, check_numerics) and returns: loss_value, tf