class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
docstr: Action distribution function for this Policy.
  
  Args:
      model: Underlying model.
      obs_batch: Observation tensor batch.
      state_batches: Action sampling state batch.
  
  Returns:
      Distribution input.
      ActionDistribution class.
      State outs.
docstr_tok: ["Action","distribution","function","for","this","Policy",".","Args",":","model",":","Underlying","model",".","obs_batch",":","Observation","tensor","batch",".","state_batches",":","Action","sampling","state","batch",".","Returns",":","Distribution","input",".","ActionDistribution","class",".","State","outs","."]
code: def action_distribution_fn(
      self,
      model: ModelV2,
      *,
      obs_batch: TensorType,
      state_batches: TensorType,
      **kwargs,
  ) -> Tuple[TensorType, type, List[TensorType]]:
      """Action distribution function for this Policy.
  
      Args:
          model: Underlying model.
          obs_batch: Observation tensor batch.
          state_batches: Action sampling state batch.
  
      Returns:
          Distribution input.
          ActionDistribution class.
          State outs.
      """
      return None, None, None
  
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 11 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj.action_distribution_fn(model) and returns: None,  None,  None