code: def _get_slice(
      self,
      slice_,
      fill=None,
      neg_indices_left_of_zero=False,
      one_hot_discrete=False,
  ):
      (
          slice_,
          slice_len,
          fill_left_count,
          fill_right_count,
      ) = self._interpret_slice(slice_, neg_indices_left_of_zero)
  
      # Perform the actual slice.
      if self.finalized:
          data_slice = tree.map_structure(
              lambda s: s[slice_], self.data
          )
      else:
          data_slice = self.data[slice_]
  
      if one_hot_discrete and slice_len > 0:
          data_slice = self._one_hot(
              data_slice, space_struct=self.space_struct
          )
  
      # Data is shorter than the range requested -> Fill the rest with `fill` data.
      if fill is not None and (
          fill_right_count > 0 or fill_left_count > 0
      ):
          if self.finalized:
              if fill_left_count:
                  fill_batch = get_dummy_batch_for_space(
                      self.space,
                      fill_value=fill,
                      batch_size=fill_left_count,
                      one_hot_discrete=one_hot_discrete,
                  )
                  if data_slice is not None:
                      data_slice = tree.map_structure(
                          lambda s0, s: np.concatenate([s0, s]),
                          fill_batch,
                          data_slice,
                      )
                  else:
                      data_slice = fill_batch
              if fill_right_count:
                  fill_batch = get_dummy_batch_for_space(
                      self.space,
                      fill_value=fill,
                      batch_size=fill_right_count,
                      one_hot_discrete=one_hot_discrete,
                  )
                  if data_slice is not None:
                      data_slice = tree.map_structure(
                          lambda s0, s: np.concatenate([s, s0]),
                          fill_batch,
                          data_slice,
                      )
                  else:
                      data_slice = fill_batch
  
          else:
              fill_batch = [
                  get_dummy_batch_for_space(
                      self.space,
                      fill_value=fill,
                      batch_size=0,
                      one_hot_discrete=one_hot_discrete,
                  )
              ]
              data_slice = (
                  fill_batch * fill_left_count
                  + (data_slice if data_slice is not None else [])
                  + fill_batch * fill_right_count
              )
  
      return data_slice
  
imports: import logging
  from typing import List, Optional, Type, Union
  import gymnasium
  import numpy
  import tree
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.env.wrappers.multi_agent_env_compatibility import MultiAgentEnvCompatibility
  from ray.rllib.utils.error import ERR_MSG_INVALID_ENV_DESCRIPTOR, ERR_MSG_OLD_GYM_API, EnvError
  from ray.rllib.utils.gym import check_old_gym_env
  from ray.rllib.utils.numpy import one_hot, one_hot_multidiscrete
  from ray.rllib.utils.spaces.space_utils import batch, get_dummy_batch_for_space, get_base_struct_from_space
  from ray.util import log_once
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 14 for calling the BufferWithInfiniteLookback obj's underlying member method code with example usage: obj._get_slice(slice_, fill, neg_indices_left_of_zero, one_hot_discrete) and returns: data_slice