code: def explained_variance(y: TensorType, pred: TensorType) -> TensorType:
      """Computes the explained variance for a pair of labels and predictions.
  
      The formula used is:
      max(-1.0, 1.0 - (std(y - pred)^2 / std(y)^2))
  
      Args:
          y: The labels.
          pred: The predictions.
  
      Returns:
          The explained variance given a pair of labels and predictions.
      """
      y_var = torch.var(y, dim=[0])
      diff_var = torch.var(y - pred, dim=[0])
      min_ = torch.tensor([-1.0]).to(pred.device)
      return torch.max(min_, 1 - (diff_var / y_var + SMALL_NUMBER))[0]
  
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 8 for calling a global function with example usage: explained_variance(y, pred) and returns: torch