class_docstr: Implements Decision Transformer: https://arxiv.org/abs/2106.01345.
class_docstr_tok: ["Implements","Decision","Transformer",":","https",":","\/\/arxiv.org\/abs\/2106.01345","."]
docstr: Get the initial input_dict to be passed into compute_single_action.
  
  Args:
      observation: first (unbatched) observation from env.reset()
      policy_id: Policy to query (only applies to multi-agent).
          Default: "default_policy".
  
  Returns:
      The input_dict for inference.
docstr_tok: ["Get","the","initial","input_dict","to","be","passed","into","compute_single_action",".","Args",":","observation",":","first","(","unbatched",")","observation","from","env.reset","(",")","policy_id",":","Policy","to","query","(","only","applies","to","multi-agent",")",".","Default",":","``","default_policy","''",".","Returns",":","The","input_dict","for","inference","."]
code: def get_initial_input_dict(
      self,
      observation: TensorStructType,
      policy_id: PolicyID = DEFAULT_POLICY_ID,
  ) -> SampleBatch:
      """Get the initial input_dict to be passed into compute_single_action.
  
      Args:
          observation: first (unbatched) observation from env.reset()
          policy_id: Policy to query (only applies to multi-agent).
              Default: "default_policy".
  
      Returns:
          The input_dict for inference.
      """
      policy = self.get_policy(policy_id)
      return policy.get_initial_input_dict(observation)
  
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 DT obj's underlying member method code with example usage: obj.get_initial_input_dict(observation, policy_id) and returns: policy