docstr: Computes the output of a forward pass of the decision transformer.
  
  Args:
      model_out: output observation tensor from the base model, [B, T, obs_dim].
      input_dict: a SampleBatch containing
          RETURNS_TO_GO: [B, T (or T + 1), 1] of returns to go values.
          ACTIONS: [B, T, action_dim] of actions.
          T: [B, T] of timesteps.
          ATTENTION_MASKS: [B, T] of attention masks.
      return_attentions: Whether to return the attention tensors from the
          transformer or not.
  
  Returns:
      A dictionary with keys and values:
          ACTIONS: [B, T, action_dim] of predicted actions.
          if return_attentions:
              "attentions": List of attentions tensors from the transformer.
          if model_config["use_obs_output"].
              OBS: [B, T, obs_dim] of predicted observations.
          if model_config["use_return_output"].
              RETURNS_to_GO: [B, T, 1] of predicted returns to go.
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code: def get_prediction(
      self,
      model_out: TensorType,
      input_dict: SampleBatch,
      return_attentions: bool = False,
  ) -> Dict[str, TensorType]:
      """Computes the output of a forward pass of the decision transformer.
  
      Args:
          model_out: output observation tensor from the base model, [B, T, obs_dim].
          input_dict: a SampleBatch containing
              RETURNS_TO_GO: [B, T (or T + 1), 1] of returns to go values.
              ACTIONS: [B, T, action_dim] of actions.
              T: [B, T] of timesteps.
              ATTENTION_MASKS: [B, T] of attention masks.
          return_attentions: Whether to return the attention tensors from the
              transformer or not.
  
      Returns:
          A dictionary with keys and values:
              ACTIONS: [B, T, action_dim] of predicted actions.
              if return_attentions:
                  "attentions": List of attentions tensors from the transformer.
              if model_config["use_obs_output"].
                  OBS: [B, T, obs_dim] of predicted observations.
              if model_config["use_return_output"].
                  RETURNS_to_GO: [B, T, 1] of predicted returns to go.
      """
      B, T, *_ = model_out.shape
  
      obs_embeds = self.obs_encoder(model_out)
      actions_embeds = self.action_encoder(
          input_dict[SampleBatch.ACTIONS]
      )
      # Note: rtg might have an extra element at the end for targets
      # During training rtg will have T + 1 for its time dimension to get the
      # rtg regression target. During evaluation/inference rtg will have T for
      # its time dimension as we don't need to call get_targets.
      returns_embeds = self.return_encoder(
          input_dict[SampleBatch.RETURNS_TO_GO][:, :T, :]
      )
      timestep_embeds = self.position_encoder(input_dict[SampleBatch.T])
  
      obs_embeds = obs_embeds + timestep_embeds
      actions_embeds = actions_embeds + timestep_embeds
      returns_embeds = returns_embeds + timestep_embeds
  
      # This makes the sequence look like (R_1, s_1, a_1, R_2, s_2, a_2, ...)
      stacked_inputs = torch.stack(
          (returns_embeds, obs_embeds, actions_embeds), dim=2
      ).reshape(B, 3 * T, self.embed_dim)
  
      attention_masks = input_dict[SampleBatch.ATTENTION_MASKS]
      stacked_attention_masks = torch.stack(
          (attention_masks, attention_masks, attention_masks), dim=2
      ).reshape(B, 3 * T)
  
      # forward the transformer model
      output_embeds = self.transformer(
          stacked_inputs,
          attention_masks=stacked_attention_masks,
          return_attentions=return_attentions,
      )
  
      outputs = {}
      if return_attentions:
          output_embeds, attentions = output_embeds
          outputs["attentions"] = attentions
  
      # compute output heads
      outputs[SampleBatch.ACTIONS] = self.action_head(
          output_embeds[:, 1::3, :]
      )
      if self.model_config["use_obs_output"]:
          outputs[SampleBatch.OBS] = self.obs_head(
              output_embeds[:, 0::3, :]
          )
      if self.model_config["use_return_output"]:
          outputs[SampleBatch.RETURNS_TO_GO] = self.return_head(
              output_embeds[:, 2::3, :]
          )
  
      return outputs
  
imports: from typing import Dict, List
  import gymnasium
  import numpy
  from gymnasium.spaces import Box, Discrete
  from ray.rllib import SampleBatch
  from ray.rllib.models import ModelV2
  from ray.rllib.models.torch.mingpt import GPT, GPTConfig
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 11 for calling the DTTorchModel obj's underlying member method code with example usage: obj.get_prediction(model_out, input_dict, return_attentions) and returns: outputs