class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
code: def maybe_initialize_optimizer_and_loss(self):
      # We don't need to initialize loss calculation for MultiGPUTowerStack.
      if self._is_tower:
          self.get_session().run(tf1.global_variables_initializer())
          return
  
      # Loss initialization and model/postprocessing test calls.
      self._init_optimizers()
      self._initialize_loss_from_dummy_batch(
          auto_remove_unneeded_view_reqs=True
      )
  
      # Create MultiGPUTowerStacks, if we have at least one actual
      # GPU or >1 CPUs (fake GPUs).
      if len(self.devices) > 1 or any("gpu" in d for d in self.devices):
          # Per-GPU graph copies created here must share vars with the
          # policy. Therefore, `reuse` is set to tf1.AUTO_REUSE because
          # Adam nodes are created after all of the device copies are
          # created.
          with tf1.variable_scope("", reuse=tf1.AUTO_REUSE):
              self.multi_gpu_tower_stacks = [
                  TFMultiGPUTowerStack(policy=self)
                  for _ in range(
                      self.config.get("num_multi_gpu_tower_stacks", 1)
                  )
              ]
  
      # Initialize again after loss and tower init.
      self.get_session().run(tf1.global_variables_initializer())
  
imports: from collections import OrderedDict
  import gymnasium
  import logging
  import re
  import tree
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type, Union
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.policy.dynamic_tf_policy import TFMultiGPUTowerStack
  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.utils import force_list
  from ray.rllib.utils.annotations import DeveloperAPI, OverrideToImplementCustomLogic, OverrideToImplementCustomLogic_CallToSuperRecommended, is_overridden, override
  from ray.rllib.utils.debug import summarize
  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.metrics.learner_info import LEARNER_STATS_KEY
  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 AlgorithmConfigDict, LocalOptimizer, ModelGradients, TensorType
  from ray.util.debug import log_once
label_desc: Use image node_id 25 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj.maybe_initialize_optimizer_and_loss() without return types