docstr: Returns a initial/target returns-to-go tensor of the given shape.
  
  Args:
      shape: Shape of the rtg tensor.
      dtype: Type of the data in the tensor. Defaults to torch.float32.
      device: The device this tensor should be on. Defaults to self.device.
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code: def get_initial_rtg_tensor(
      self,
      shape: TensorShape,
      dtype: Optional[Type] = torch.float32,
      device: Optional["torch.device"] = None,
  ):
      """Returns a initial/target returns-to-go tensor of the given shape.
  
      Args:
          shape: Shape of the rtg tensor.
          dtype: Type of the data in the tensor. Defaults to torch.float32.
          device: The device this tensor should be on. Defaults to self.device.
      """
      if device is None:
          device = self.device
      if dtype is None:
          device = torch.float32
  
      assert (
          self.config["target_return"] is not None
      ), "Must specify target_return."
      initial_rtg = torch.full(
          shape,
          fill_value=self.config["target_return"],
          dtype=dtype,
          device=device,
      )
      return initial_rtg
  
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 7 for calling the DTTorchPolicy obj's underlying member method code with example usage: obj.get_initial_rtg_tensor(shape, dtype, device) and returns: initial_rtg