class_docstr: Collects samples for one agent in one trajectory (episode).
  
  The agent may be part of a multi-agent environment. Samples are stored in
  lists including some possible automatic "shift" buffer at the beginning to
  be able to save memory when storing things like NEXT_OBS, PREV_REWARDS,
  etc.., which are specified using the trajectory view API.
class_docstr_tok: ["Collects","samples","for","one","agent","in","one","trajectory","(","episode",")",".","The","agent","may","be","part","of","a","multi-agent","environment",".","Samples","are","stored","in","lists","including","some","possible","automatic","``","shift","''","buffer","at","the","beginning","to","be","able","to","save","memory","when","storing","things","like","NEXT_OBS",",","PREV_REWARDS",",","etc","..",",","which","are","specified","using","the","trajectory","view","API","."]
code: def _prepare_for_data_cols_with_dummy_values(self, data_col):
      self.data_cols_with_dummy_values.add(data_col)
      # For items gained during inference, we append a dummy value here so
      # that view requirements viewing these is not shifted by 1
      for b in self.buffers[data_col]:
          b.append(b[-1])
  
imports: import copy
  import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import tree
  from gymnasium.spaces import Space
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.space_utils import flatten_to_single_ndarray, get_dummy_batch_for_space
  from ray.rllib.utils.typing import EpisodeID, EnvID, TensorType, ViewRequirementsDict
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 14 for calling the AgentCollector obj's underlying member method code with example usage: obj._prepare_for_data_cols_with_dummy_values(data_col) without return types