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 = tf.nn.moments(y, axes=[0])
      _, diff_var = tf.nn.moments(y - pred, axes=[0])
      return tf.maximum(-1.0, 1 - (diff_var / y_var))
  
imports: import logging
  from typing import Any, Callable, List, Optional, Type, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, PartialAlgorithmConfigDict, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 2 for calling a global function with example usage: explained_variance(y, pred) and returns: tf