code: def compute_grad_norm(parameters, norm_type=2.0):
      """
      Compute norm over gradients of model parameters.
  
      :param parameters:
          the model parameters for gradient norm calculation. Iterable of
          Tensors or single Tensor
      :param norm_type:
          type of p-norm to use
  
      :returns:
          the computed gradient norm
      """
      if isinstance(parameters, torch.Tensor):
          parameters = [parameters]
      parameters = [
          p for p in parameters if p is not None and p.grad is not None
      ]
      total_norm = 0
      for p in parameters:
          param_norm = p.grad.data.norm(norm_type)
          total_norm += param_norm.item() ** norm_type
      return total_norm ** (1.0 / norm_type)
  
imports: import os
  from typing import Union, Optional, Tuple, Any, List, Sized, TypeVar
  import itertools
  from collections import namedtuple
  import parlai.utils.logging
  import parlai.utils.io
  import torch.optim
label_desc: Use image node_id 7 for calling a global function with example usage: compute_grad_norm(parameters, norm_type) and returns: unknown