class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  Do not sub-class this class directly (neither should you sub-class
  TFPolicy), but rather use rllib.policy.tf_policy_template.build_tf_policy
  to generate your custom tf (graph-mode or eager) Policy classes.
code: def learn_on_loaded_batch(
      self, offset: int = 0, buffer_index: int = 0
  ):
      # Shortcut for 1 CPU only: Batch should already be stored in
      # `self._loaded_single_cpu_batch`.
      if len(self.devices) == 1 and self.devices[0] == "/cpu:0":
          assert buffer_index == 0
          if self._loaded_single_cpu_batch is None:
              raise ValueError(
                  "Must call Policy.load_batch_into_buffer() before "
                  "Policy.learn_on_loaded_batch()!"
              )
          # Get the correct slice of the already loaded batch to use,
          # based on offset and batch size.
          batch_size = self.config.get(
              "sgd_minibatch_size", self.config["train_batch_size"]
          )
          if batch_size >= len(self._loaded_single_cpu_batch):
              sliced_batch = self._loaded_single_cpu_batch
          else:
              sliced_batch = self._loaded_single_cpu_batch.slice(
                  start=offset, end=offset + batch_size
              )
          return self.learn_on_batch(sliced_batch)
  
      tower_stack = self.multi_gpu_tower_stacks[buffer_index]
      results = tower_stack.optimize(self.get_session(), offset)
      self.num_grad_updates += 1
  
      results.update(
          {
              NUM_GRAD_UPDATES_LIFETIME: self.num_grad_updates,
              # -1, b/c we have to measure this diff before we do the update above.
              DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY: (
                  self.num_grad_updates
                  - 1
                  - (tower_stack.num_grad_updates or 0)
              ),
          }
      )
  
      return results
  
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 6 for calling the DynamicTFPolicy obj's underlying member method code with example usage: obj.learn_on_loaded_batch(offset, buffer_index) and returns: results, self