class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
docstr: Creates input_dict and dummy_batch for loss initialization.
  
  Used for managing the Policy's input placeholders and for loss
  initialization.
  Input_dict: Str -> tf.placeholders, dummy_batch: str -> np.arrays.
  
  Args:
      view_requirements: The view requirements dict.
      existing_inputs (Dict[str, tf.placeholder]): A dict of already
          existing placeholders.
  
  Returns:
      Tuple[Dict[str, tf.placeholder], Dict[str, np.ndarray]]: The
          input_dict/dummy_batch tuple.
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code: def _create_input_dict_and_dummy_batch(
      self, view_requirements, existing_inputs
  ):
      """Creates input_dict and dummy_batch for loss initialization.
  
      Used for managing the Policy's input placeholders and for loss
      initialization.
      Input_dict: Str -> tf.placeholders, dummy_batch: str -> np.arrays.
  
      Args:
          view_requirements: The view requirements dict.
          existing_inputs (Dict[str, tf.placeholder]): A dict of already
              existing placeholders.
  
      Returns:
          Tuple[Dict[str, tf.placeholder], Dict[str, np.ndarray]]: The
              input_dict/dummy_batch tuple.
      """
      input_dict = {}
      for view_col, view_req in view_requirements.items():
          # Point state_in to the already existing self._state_inputs.
          mo = re.match(r"state_in_(\d+)", view_col)
          if mo is not None:
              input_dict[view_col] = self._state_inputs[
                  int(mo.group(1))
              ]
          # State-outs (no placeholders needed).
          elif view_col.startswith("state_out_"):
              continue
          # Skip action dist inputs placeholder (do later).
          elif view_col == SampleBatch.ACTION_DIST_INPUTS:
              continue
          # This is a tower: Input placeholders already exist.
          elif view_col in existing_inputs:
              input_dict[view_col] = existing_inputs[view_col]
          # All others.
          else:
              time_axis = not isinstance(view_req.shift, int)
              if view_req.used_for_training:
                  # Create a +time-axis placeholder if the shift is not an
                  # int (range or list of ints).
                  # Do not flatten actions if action flattening disabled.
                  if self.config.get(
                      "_disable_action_flattening"
                  ) and view_col in [
                      SampleBatch.ACTIONS,
                      SampleBatch.PREV_ACTIONS,
                  ]:
                      flatten = False
                  # Do not flatten observations if no preprocessor API used.
                  elif (
                      view_col
                      in [SampleBatch.OBS, SampleBatch.NEXT_OBS]
                      and self.config["_disable_preprocessor_api"]
                  ):
                      flatten = False
                  # Flatten everything else.
                  else:
                      flatten = True
                  input_dict[view_col] = get_placeholder(
                      space=view_req.space,
                      name=view_col,
                      time_axis=time_axis,
                      flatten=flatten,
                  )
      dummy_batch = self._get_dummy_batch_from_view_requirements(
          batch_size=32
      )
  
      return (
          SampleBatch(input_dict, seq_lens=self._seq_lens),
          dummy_batch,
      )
  
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 22 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj._create_input_dict_and_dummy_batch(view_requirements, existing_inputs) and returns: SampleBatch,  dummy_batch