docstr: Compute the target predictions for a given input_dict.
  
  Args:
      model_out: output observation tensor from the base model, [B, T, obs_dim].
      input_dict: a SampleBatch containing
          RETURNS_TO_GO: [B, 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.
  
  Returns:
      A dictionary with keys and values:
          ACTIONS: [B, T, action_dim] of target actions.
          if model_config["use_obs_output"]
              OBS: [B, T, obs_dim] of target observations.
          if model_config["use_return_output"]
              RETURNS_to_GO: [B, T, 1] of target returns to go.
docstr_tok: ["Compute","the","target","predictions","for","a","given","input_dict",".","Args",":","model_out",":","output","observation","tensor","from","the","base","model",",","[","B",",","T",",","obs_dim","]",".","input_dict",":","a","SampleBatch","containing","RETURNS_TO_GO",":","[","B",",","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",".","Returns",":","A","dictionary","with","keys","and","values",":","ACTIONS",":","[","B",",","T",",","action_dim","]","of","target","actions",".","if","model_config","[","``","use_obs_output","''","]","OBS",":","[","B",",","T",",","obs_dim","]","of","target","observations",".","if","model_config","[","``","use_return_output","''","]","RETURNS_to_GO",":","[","B",",","T",",","1","]","of","target","returns","to","go","."]
code: def get_targets(
      self, model_out: TensorType, input_dict: SampleBatch
  ) -> Dict[str, TensorType]:
      """Compute the target predictions for a given input_dict.
  
      Args:
          model_out: output observation tensor from the base model, [B, T, obs_dim].
          input_dict: a SampleBatch containing
              RETURNS_TO_GO: [B, 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.
  
      Returns:
          A dictionary with keys and values:
              ACTIONS: [B, T, action_dim] of target actions.
              if model_config["use_obs_output"]
                  OBS: [B, T, obs_dim] of target observations.
              if model_config["use_return_output"]
                  RETURNS_to_GO: [B, T, 1] of target returns to go.
      """
      targets = {
          SampleBatch.ACTIONS: input_dict[SampleBatch.ACTIONS].detach()
      }
      if self.model_config["use_obs_output"]:
          targets[SampleBatch.OBS] = model_out.detach()
      if self.model_config["use_return_output"]:
          targets[SampleBatch.RETURNS_TO_GO] = input_dict[
              SampleBatch.RETURNS_TO_GO
          ][:, 1:, :].detach()
  
      return targets
  
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 12 for calling the DTTorchModel obj's underlying member method code with example usage: obj.get_targets(model_out, input_dict) and returns: targets