class_docstr: Optimizer that runs in parallel across multiple local devices.
  
  TFMultiGPUTowerStack automatically splits up and loads training data
  onto specified local devices (e.g. GPUs) with `load_data()`. During a call
  to `optimize()`, the devices compute gradients over slices of the data in
  parallel. The gradients are then averaged and applied to the shared
  weights.
  
  The data loaded is pinned in device memory until the next call to
  `load_data`, so you can make multiple passes (possibly in randomized order)
  over the same data once loaded.
  
  This is similar to tf1.train.SyncReplicasOptimizer, but works within a
  single TensorFlow graph, i.e. implements in-graph replicated training:
  
  https://www.tensorflow.org/api_docs/python/tf/train/SyncReplicasOptimizer
docstr: Run a single step of SGD.
  
  Runs a SGD step over a slice of the preloaded batch with size given by
  self._loaded_per_device_batch_size and offset given by the batch_index
  argument.
  
  Updates shared model weights based on the averaged per-device
  gradients.
  
  Args:
      sess: TensorFlow session.
      batch_index: Offset into the preloaded data. This value must be
          between `0` and `tuples_per_device`. The amount of data to
          process is at most `max_per_device_batch_size`.
  
  Returns:
      The outputs of extra_ops evaluated over the batch.
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code: def optimize(self, sess, batch_index):
      """Run a single step of SGD.
  
      Runs a SGD step over a slice of the preloaded batch with size given by
      self._loaded_per_device_batch_size and offset given by the batch_index
      argument.
  
      Updates shared model weights based on the averaged per-device
      gradients.
  
      Args:
          sess: TensorFlow session.
          batch_index: Offset into the preloaded data. This value must be
              between `0` and `tuples_per_device`. The amount of data to
              process is at most `max_per_device_batch_size`.
  
      Returns:
          The outputs of extra_ops evaluated over the batch.
      """
      feed_dict = {
          self._batch_index: batch_index,
          self._per_device_batch_size: self._loaded_per_device_batch_size,
          self._max_seq_len: self._loaded_max_seq_len,
      }
      for tower in self._towers:
          feed_dict.update(
              tower.loss_graph.extra_compute_grad_feed_dict()
          )
  
      fetches = {"train": self._train_op}
      for tower_num, tower in enumerate(self._towers):
          tower_fetch = tower.loss_graph._get_grad_and_stats_fetches()
          fetches["tower_{}".format(tower_num)] = tower_fetch
  
      return sess.run(fetches, feed_dict=feed_dict)
  
imports: from collections import namedtuple, OrderedDict
  import gymnasium
  import logging
  import re
  import tree
  from typing import Callable, Dict, List, Optional, Tuple, Type, Union
  from ray.util.debug import log_once
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.utils import force_list
  from ray.rllib.utils.annotations import override, DeveloperAPI
  from ray.rllib.utils.debug import summarize
  from ray.rllib.utils.deprecation import deprecation_warning, DEPRECATED_VALUE
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.metrics import DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_GRAD_UPDATES_LIFETIME
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.tf_utils import get_placeholder
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 3 for calling the TFMultiGPUTowerStack obj's underlying member method code with example usage: obj.optimize(sess, batch_index) and returns: sess