class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
docstr: Initialize input placeholders.
  
  Args:
      existing_inputs: existing placeholders.
docstr_tok: ["Initialize","input","placeholders",".","Args",":","existing_inputs",":","existing","placeholders","."]
code: def _init_state_inputs(
      self, existing_inputs: Dict[str, "tf1.placeholder"]
  ):
      """Initialize input placeholders.
  
      Args:
          existing_inputs: existing placeholders.
      """
      if existing_inputs:
          self._state_inputs = [
              v
              for k, v in existing_inputs.items()
              if k.startswith("state_in_")
          ]
          # Placeholder for RNN time-chunk valid lengths.
          if self._state_inputs:
              self._seq_lens = existing_inputs[SampleBatch.SEQ_LENS]
          # Create new input placeholders.
      else:
          self._state_inputs = [
              get_placeholder(
                  space=vr.space,
                  time_axis=not isinstance(vr.shift, int),
                  name=k,
              )
              for k, vr in self.model.view_requirements.items()
              if k.startswith("state_in_")
          ]
          # Placeholder for RNN time-chunk valid lengths.
          if self._state_inputs:
              self._seq_lens = tf1.placeholder(
                  dtype=tf.int32, shape=[None], name="seq_lens"
              )
  
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 20 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj._init_state_inputs(existing_inputs) without return types