code: def _compute_action_helper(self, input_dict):
      # Switch to eval mode.
      self.model.eval()
  
      batch_size = input_dict[SampleBatch.OBS].shape[0]
  
      # NOTE: This is probably the most confusing part of the code, made to work with
      # env_runner and SimpleListCollector during evaluation, and thus should
      # be changed for the new Policy and Connector API.
      # So I'll explain how it works.
  
      # Add current timestep (+1 because -1 is first observation)
      # NOTE: ViewRequirement of timestep is -(max_seq_len-2):0.
      # The wierd limits is because RLlib treats initial obs as time -1,
      # and then 0 is (act, rew, next_obs), etc.
      # So we only collect max_seq_len-1 from the rollout and create the current
      # step here by adding 1.
      # Decision transformer treats initial observation as timestep 0, giving us
      # 0 is (obs, act, rew).
      timesteps = input_dict[SampleBatch.T]
      new_timestep = timesteps[:, -1:] + 1
      input_dict[SampleBatch.T] = torch.cat(
          [timesteps, new_timestep], dim=1
      )
  
      # mask out any padded value at start of rollout
      # NOTE: the other reason for doing this is that evaluation rollout front
      # pads timesteps with -1, so using this we can find out when we need to mask
      # out the front section of the batch.
      input_dict[SampleBatch.ATTENTION_MASKS] = torch.where(
          input_dict[SampleBatch.T] >= 0, 1.0, 0.0
      )
  
      # Remove out-of-bound -1 timesteps after attention mask is calculated
      uncliped_timesteps = input_dict[SampleBatch.T]
      input_dict[SampleBatch.T] = torch.where(
          uncliped_timesteps < 0,
          torch.zeros_like(uncliped_timesteps),
          uncliped_timesteps,
      )
  
      # Computes returns-to-go.
      # NOTE: There are two rtg calculations: updated_rtg and initial_rtg.
      # updated_rtg takes the previous rtg value (the ViewRequirement is
      # -(max_seq_len-1):-1), and subtracts the last reward from it.
      rtg = input_dict[SampleBatch.RETURNS_TO_GO]
      last_rtg = rtg[:, -1]
      last_reward = input_dict[SampleBatch.REWARDS]
      updated_rtg = last_rtg - last_reward
      # initial_rtg simply is filled with target_return.
      # These two are both only for the current timestep.
      initial_rtg = self.get_initial_rtg_tensor(
          (batch_size, 1), dtype=rtg.dtype, device=rtg.device
      )
  
      # Then based on whether we are currently at the first timestep or not
      # we use the initial_rtg or updated_rtg.
      new_rtg = torch.where(
          new_timestep == 0, initial_rtg, updated_rtg[:, None]
      )
      # Append the new_rtg to the batch.
      input_dict[SampleBatch.RETURNS_TO_GO] = torch.cat(
          [rtg, new_rtg], dim=1
      )[..., None]
  
      # Pad current action (is not actually attended to and used during inference)
      past_actions = input_dict[SampleBatch.ACTIONS]
      action_pad = torch.zeros(
          (batch_size, 1, *past_actions.shape[2:]),
          dtype=past_actions.dtype,
          device=past_actions.device,
      )
      input_dict[SampleBatch.ACTIONS] = torch.cat(
          [past_actions, action_pad], dim=1
      )
  
      # Run inference on model
      model_out, _ = self.model(input_dict)  # noop, just returns obs.
      preds = self.model.get_prediction(model_out, input_dict)
      dist_inputs = preds[SampleBatch.ACTIONS][:, -1]
  
      # Get the actions from the action_dist.
      action_dist = self.dist_class(dist_inputs, self.model)
      actions = action_dist.deterministic_sample()
  
      # This is used by env_runner and is actually how it adds custom keys to
      # SimpleListCollector and allows ViewRequirements to work.
      # This is also used in user inference in get_next_input_dict, which takes
      # this output as one of the input.
      extra_fetches = {
          # new_rtg still has the leftover extra 3rd dimension for inference
          SampleBatch.RETURNS_TO_GO: new_rtg.squeeze(-1),
          SampleBatch.ACTION_DIST_INPUTS: dist_inputs,
      }
  
      # Update our global timestep by the batch size.
      self.global_timestep += len(input_dict[SampleBatch.CUR_OBS])
  
      return convert_to_numpy((actions, [], extra_fetches))
  
imports: from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Box, Discrete
  from rllib_dt.dt.dt_torch_model import DTTorchModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.mingpt import configure_gpt_optimizer
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDeterministic, TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI, override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.threading import with_lock
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  from ray.rllib.utils.typing import TensorShape, TensorStructType, TensorType, TrainerConfigDict
label_desc: Use image node_id 10 for calling the DTTorchPolicy obj's underlying member method code with example usage: obj._compute_action_helper(input_dict) and returns: convert_to_numpy