code: def global_norm(tensors: List[TensorType]) -> TensorType:
      """Returns the global L2 norm over a list of tensors.
  
      output = sqrt(SUM(t ** 2 for t in tensors)),
          where SUM reduces over all tensors and over all elements in tensors.
  
      Args:
          tensors: The list of tensors to calculate the global norm over.
  
      Returns:
          The global L2 norm over the given tensor list.
      """
      # List of single tensors' L2 norms: SQRT(SUM(xi^2)) over all xi in tensor.
      single_l2s = [
          torch.pow(torch.sum(torch.pow(t, 2.0)), 0.5) for t in tensors
      ]
      # Compute global norm from all single tensors' L2 norms.
      return torch.pow(
          sum(torch.pow(l2, 2.0) for l2 in single_l2s), 0.5
      )
  
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 11 for calling a global function with example usage: global_norm(tensors) and returns: torch