code: def _batch_inference_sample_batches(
      eval_data: List[SampleBatch],
  ) -> SampleBatch:
      """Batch a list of input SampleBatches into a single SampleBatch.
  
      Args:
          eval_data: list of SampleBatches.
  
      Returns:
          single batched SampleBatch.
      """
      inference_batch = concat_samples(eval_data)
      if "state_in_0" in inference_batch:
          batch_size = len(eval_data)
          inference_batch[SampleBatch.SEQ_LENS] = np.ones(
              batch_size, dtype=np.int32
          )
      return inference_batch
  
imports: from collections import defaultdict
  import logging
  import time
  import tree
  from typing import TYPE_CHECKING, Dict, Iterator, List, Optional, Set, Tuple, Union
  import numpy
  from ray.rllib.env.base_env import ASYNC_RESET_RETURN, BaseEnv
  from ray.rllib.env.external_env import ExternalEnvWrapper
  from ray.rllib.env.wrappers.atari_wrappers import MonitorEnv, get_wrapper_by_cls
  from ray.rllib.evaluation.collectors.simple_list_collector import _PolicyCollectorGroup
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.evaluation.episode_v2 import EpisodeV2
  from ray.rllib.evaluation.metrics import RolloutMetrics
  from ray.rllib.models.preprocessors import Preprocessor
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch, concat_samples
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.filter import Filter
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.space_utils import unbatch, get_original_space
  from ray.rllib.utils.typing import ActionConnectorDataType, AgentConnectorDataType, AgentID, EnvActionType, EnvID, EnvInfoDict, EnvObsType, MultiAgentDict, MultiEnvDict, PolicyID, PolicyOutputType, SampleBatchType, StateBatches, TensorStructType
  from ray.util.debug import log_once
label_desc: Use image node_id 2 for calling a global function with example usage: _batch_inference_sample_batches(eval_data) and returns: inference_batch