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 a copy of self using existing input placeholders.
docstr_tok: ["Creates","a","copy","of","self","using","existing","input","placeholders","."]
code: def copy(
      self, existing_inputs: List[Tuple[str, "tf1.placeholder"]]
  ) -> TFPolicy:
      """Creates a copy of self using existing input placeholders."""
  
      flat_loss_inputs = tree.flatten(self._loss_input_dict)
      flat_loss_inputs_no_rnn = tree.flatten(
          self._loss_input_dict_no_rnn
      )
  
      # Note that there might be RNN state inputs at the end of the list
      if len(flat_loss_inputs) != len(existing_inputs):
          raise ValueError(
              "Tensor list mismatch",
              self._loss_input_dict,
              self._state_inputs,
              existing_inputs,
          )
      for i, v in enumerate(flat_loss_inputs_no_rnn):
          if v.shape.as_list() != existing_inputs[i].shape.as_list():
              raise ValueError(
                  "Tensor shape mismatch",
                  i,
                  v.shape,
                  existing_inputs[i].shape,
              )
      # By convention, the loss inputs are followed by state inputs and then
      # the seq len tensor.
      rnn_inputs = []
      for i in range(len(self._state_inputs)):
          rnn_inputs.append(
              (
                  "state_in_{}".format(i),
                  existing_inputs[len(flat_loss_inputs_no_rnn) + i],
              )
          )
      if rnn_inputs:
          rnn_inputs.append((SampleBatch.SEQ_LENS, existing_inputs[-1]))
      existing_inputs_unflattened = tree.unflatten_as(
          self._loss_input_dict_no_rnn,
          existing_inputs[: len(flat_loss_inputs_no_rnn)],
      )
      input_dict = OrderedDict(
          [
              ("is_exploring", self._is_exploring),
              ("timestep", self._timestep),
          ]
          + [
              (k, existing_inputs_unflattened[k])
              for i, k in enumerate(self._loss_input_dict_no_rnn.keys())
          ]
          + rnn_inputs
      )
  
      instance = self.__class__(
          self.observation_space,
          self.action_space,
          self.config,
          existing_inputs=input_dict,
          existing_model=[
              self.model,
              # Deprecated: Target models should all reside under
              # `policy.target_model` now.
              ("target_q_model", getattr(self, "target_q_model", None)),
              ("target_model", getattr(self, "target_model", None)),
          ],
      )
  
      instance._loss_input_dict = input_dict
      losses = instance._do_loss_init(SampleBatch(input_dict))
      loss_inputs = [
          (k, existing_inputs_unflattened[k])
          for i, k in enumerate(self._loss_input_dict_no_rnn.keys())
      ]
  
      TFPolicy._initialize_loss(instance, losses, loss_inputs)
      instance._stats_fetches.update(
          instance.grad_stats_fn(input_dict, instance._grads)
      )
      return instance
  
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 28 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj.copy(existing_inputs) and returns: instance