docstr: === DT configs
  
  Args:
      target_return: The target return-to-go for inference/evaluation.
      **kwargs: Forward compatibility kwargs
  
  Returns:
      This updated DTConfig object.
docstr_tok: ["===","DT","configs","Args",":","target_return",":","The","target","return-to-go","for","inference\/evaluation",".","*","*","kwargs",":","Forward","compatibility","kwargs","Returns",":","This","updated","DTConfig","object","."]
code: def evaluation(
      self,
      *,
      target_return: Optional[float] = NotProvided,
      **kwargs,
  ) -> "DTConfig":
      """
      === DT configs
  
      Args:
          target_return: The target return-to-go for inference/evaluation.
          **kwargs: Forward compatibility kwargs
  
      Returns:
          This updated DTConfig object.
      """
      super().evaluation(**kwargs)
      if target_return is not NotProvided:
          self.target_return = target_return
  
      return self
  
imports: import logging
  import math
  from typing import Any, Dict, List, Optional, Tuple, Type, Union
  from rllib_dt.dt.segmentation_buffer import MultiAgentSegmentationBuffer
  from ray.rllib import SampleBatch
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.execution import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
  from ray.rllib.policy import Policy
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils import deep_update
  from ray.rllib.utils.annotations import PublicAPI, override
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, SAMPLE_TIMER
  from ray.rllib.utils.typing import PolicyID, ResultDict, TensorStructType, TensorType
label_desc: Use image node_id 3 for calling the DTConfig obj's underlying member method code with example usage: obj.evaluation() and returns: self