class_docstr: Hook executed on a backward pass that clips gradients such that their
  norm does not exceed a specific value. Dosovitskiy et al. found it
  to be critical for training vision transformers
  (https://arxiv.org/abs/2010.11929), but subsequent studies have been less
  clear about its importance. Gradient clipping configuration is set in
  config.MODEL.GRAD_CLIP
code: def __init__(
      self,
      norm_type: Union[int, float, str],
      max_norm: Union[int, float],
  ):
      super().__init__()
      self.norm_type = norm_type
      self.max_norm = max_norm
  
file_path: vissl/vissl/hooks/grad_clip_hooks.py
imports: from typing import Union
  import torch.nn.utils
  from classy_vision import tasks
  from classy_vision.hooks.classy_hook import ClassyHook
  from classy_vision.tasks.classification_task import AmpType
  from fairscale.nn import FullyShardedDataParallel
label_desc: Use image node_id 1 to create a new GradClipHook object from inherited base classes: ClassyHook with example: obj = GradClipHook(norm_type, max_norm)