class_docstr: Persistent buffer for main gradients that remains allocated between training iterations
docstr: Return a tensor with the input `shape` as a view into the
  1-D data starting at `start_index`.
code: def get(self, shape, start_index):
      """Return a tensor with the input `shape` as a view into the
      1-D data starting at `start_index`."""
      end_index = start_index + shape.numel()
      assert (
          end_index <= self.numel
      ), "requested tensor is out of the buffer range."
      buffer_tensor = self.data[start_index:end_index]
      buffer_tensor = buffer_tensor.view(shape)
  
      grad_chunk_info = None
      if self.chunk_size_mb > 0:
          grad_chunk_info = {}
          chunk = start_index // self.chunk_size_numel
          chunk_start_index = self.start_index_per_chunk[chunk]
          chunk_end_index = (
              chunk_start_index + self.numel_per_chunk[chunk]
          )
          grad_chunk_info[chunk] = (
              min(chunk_end_index, end_index) - start_index
          )
          while chunk_end_index < end_index:
              chunk += 1
              chunk_start_index = self.start_index_per_chunk[chunk]
              chunk_end_index = (
                  chunk_start_index + self.numel_per_chunk[chunk]
              )
              grad_chunk_info[chunk] = (
                  min(chunk_end_index, end_index) - chunk_start_index
              )
  
      return buffer_tensor, grad_chunk_info
  
imports: from contextlib import contextmanager
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the GradBucket obj's underlying member method code with example usage: obj.get(shape, start_index) and returns: buffer_tensor,  grad_chunk_info