code: def validate(self) -> None:
      # Call super's validation method.
      super().validate()
  
      # target_return must be specified
      assert (
          self.target_return is not None
      ), "Must specify a target return (total sum of rewards)."
  
      # horizon must be specified and >= 2
      assert self.horizon is not None, "Must specify rollout horizon."
      assert self.horizon >= 2, "rollout horizon must be at least 2."
  
      # replay_buffer's type must be MultiAgentSegmentationBuffer
      assert (
          self.replay_buffer_config is not None
      ), "Must specify replay_buffer_config."
      replay_buffer_type = self.replay_buffer_config.get("type")
      assert (
          replay_buffer_type == MultiAgentSegmentationBuffer
      ), "replay_buffer's type must be MultiAgentSegmentationBuffer."
  
      # max_seq_len must be specified in model
      model_max_seq_len = self.model.get("max_seq_len")
      assert (
          model_max_seq_len is not None
      ), "Must specify model's max_seq_len."
  
      # User shouldn't need to specify replay_buffer's max_seq_len.
      # Autofill for replay buffer API. If they did specify, make sure it
      # matches with model's max_seq_len
      buffer_max_seq_len = self.replay_buffer_config.get("max_seq_len")
      if buffer_max_seq_len is None:
          self.replay_buffer_config["max_seq_len"] = model_max_seq_len
      else:
          assert (
              buffer_max_seq_len == model_max_seq_len
          ), "replay_buffer's max_seq_len must equal model's max_seq_len."
  
      # Same thing for buffer's max_ep_len, which should be autofilled from
      # rollout's horizon, or check that it matches if user specified.
      buffer_max_ep_len = self.replay_buffer_config.get("max_ep_len")
      if buffer_max_ep_len is None:
          self.replay_buffer_config["max_ep_len"] = self.horizon
      else:
          assert (
              buffer_max_ep_len == self.horizon
          ), "replay_buffer's max_ep_len must equal rollout horizon."
  
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 5 for calling the DTConfig obj's underlying member method code with example usage: obj.validate() without return types