class_docstr: Persistent buffer for main gradients that remains allocated between training iterations
code: def update_chunk_info(self, grad_chunk_info):
      for chunk in grad_chunk_info.keys():
          self.computed_numel_per_chunk[chunk] += grad_chunk_info[chunk]
  
imports: from contextlib import contextmanager
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the GradBucket obj's underlying member method code with example usage: obj.update_chunk_info(grad_chunk_info) without return types