code: def softmax_cross_entropy_with_logits(
      logits: TensorType,
      labels: TensorType,
  ) -> TensorType:
      """Same behavior as tf.nn.softmax_cross_entropy_with_logits.
  
      Args:
          x: The input predictions.
          labels: The labels corresponding to `x`.
  
      Returns:
          The resulting softmax cross-entropy given predictions and labels.
      """
      return torch.sum(
          -labels * nn.functional.log_softmax(logits, -1), -1
      )
  
imports: import logging
  import os
  import warnings
  from typing import Dict, List, Optional, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from packaging import version
  import ray
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.utils.annotations import Deprecated, PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import SMALL_NUMBER
  from ray.rllib.utils.typing import LocalOptimizer, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 20 for calling a global function with example usage: softmax_cross_entropy_with_logits(logits, labels) and returns: torch