class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  Do not sub-class this class directly (neither should you sub-class
  TFPolicy), but rather use rllib.policy.tf_policy_template.build_tf_policy
  to generate your custom tf (graph-mode or eager) Policy classes.
code: def load_batch_into_buffer(
      self,
      batch: SampleBatch,
      buffer_index: int = 0,
  ) -> int:
      # Set the is_training flag of the batch.
      batch.set_training(True)
  
      # Shortcut for 1 CPU only: Store batch in
      # `self._loaded_single_cpu_batch`.
      if len(self.devices) == 1 and self.devices[0] == "/cpu:0":
          assert buffer_index == 0
          self._loaded_single_cpu_batch = batch
          return len(batch)
  
      input_dict = self._get_loss_inputs_dict(batch, shuffle=False)
      data_keys = tree.flatten(self._loss_input_dict_no_rnn)
      if self._state_inputs:
          state_keys = self._state_inputs + [self._seq_lens]
      else:
          state_keys = []
      inputs = [input_dict[k] for k in data_keys]
      state_inputs = [input_dict[k] for k in state_keys]
  
      return self.multi_gpu_tower_stacks[buffer_index].load_data(
          sess=self.get_session(),
          inputs=inputs,
          state_inputs=state_inputs,
          num_grad_updates=batch.num_grad_updates,
      )
  
imports: from collections import namedtuple, OrderedDict
  import gymnasium
  import logging
  import re
  import tree
  from typing import Callable, Dict, List, Optional, Tuple, Type, Union
  from ray.util.debug import log_once
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  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.models.catalog import ModelCatalog
  from ray.rllib.utils import force_list
  from ray.rllib.utils.annotations import override, DeveloperAPI
  from ray.rllib.utils.debug import summarize
  from ray.rllib.utils.deprecation import deprecation_warning, DEPRECATED_VALUE
  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.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.tf_utils import get_placeholder
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 4 for calling the DynamicTFPolicy obj's underlying member method code with example usage: obj.load_batch_into_buffer(batch, buffer_index) and returns: self, len