class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
docstr: Initialized input_dict and dummy_batch data.
  
  Args:
      existing_inputs: When copying a policy, this specifies an existing
          dict of placeholders to use instead of defining new ones.
  
  Returns:
      timestep: training timestep.
      explore: whether this policy should explore.
docstr_tok: ["Initialized","input_dict","and","dummy_batch","data",".","Args",":","existing_inputs",":","When","copying","a","policy",",","this","specifies","an","existing","dict","of","placeholders","to","use","instead","of","defining","new","ones",".","Returns",":","timestep",":","training","timestep",".","explore",":","whether","this","policy","should","explore","."]
code: def _init_input_dict_and_dummy_batch(
      self, existing_inputs: Dict[str, "tf1.placeholder"]
  ) -> Tuple[Union[int, TensorType], Union[bool, TensorType]]:
      """Initialized input_dict and dummy_batch data.
  
      Args:
          existing_inputs: When copying a policy, this specifies an existing
              dict of placeholders to use instead of defining new ones.
  
      Returns:
          timestep: training timestep.
          explore: whether this policy should explore.
      """
      # Setup standard placeholders.
      if self._is_tower:
          assert existing_inputs is not None
          timestep = existing_inputs["timestep"]
          explore = False
          (
              self._input_dict,
              self._dummy_batch,
          ) = self._create_input_dict_and_dummy_batch(
              self.view_requirements, existing_inputs
          )
      else:
          # Placeholder for (sampling steps) timestep (int).
          timestep = tf1.placeholder_with_default(
              tf.zeros((), dtype=tf.int64), (), name="timestep"
          )
          # Placeholder for `is_exploring` flag.
          explore = tf1.placeholder_with_default(
              True, (), name="is_exploring"
          )
          (
              self._input_dict,
              self._dummy_batch,
          ) = self._create_input_dict_and_dummy_batch(
              self.view_requirements, {}
          )
  
      # Placeholder for `is_training` flag.
      self._input_dict.set_training(self._get_is_training_placeholder())
  
      return timestep, explore
  
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 21 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj._init_input_dict_and_dummy_batch(existing_inputs) and returns: timestep,  explore