code: def get_full_hp_grad(self):
      reduce_buffer = torch.zeros_like(
          self, dtype=torch.float32
      ).flatten()
      if self._hp_mapping is not None:
          hp_mapping = self._hp_mapping
  
          if hp_mapping.use_offload:
              gradient_dict = hp_mapping.offload_gradient_dict
          else:
              gradient_dict = hp_mapping.gradient_dict
  
          if (
              hp_mapping.param_group_index not in gradient_dict
              or gradient_dict[hp_mapping.param_group_index] is None
          ):
              raise ValueError(
                  "Gradients are only available immediately after backward and before engine step"
              )
  
          lp_grad_fragment = gradient_dict[
              hp_mapping.param_group_index
          ][self._index_in_param_group]
          hp_grad_fragment = lp_grad_fragment.to(
              torch.float32
          ).flatten()
  
          lp_frag_address = self._hp_mapping.lp_fragment_address
          reduce_fragment = torch.narrow(
              reduce_buffer,
              0,
              lp_frag_address.start,
              lp_frag_address.numel,
          )
  
          if self.view(-1).shape == hp_grad_fragment.shape:
              reduce_buffer.data.copy_(hp_grad_fragment.data)
          else:
              reduce_fragment.data.copy_(hp_grad_fragment.data)
  
      dist.all_reduce(reduce_buffer, group=self._dp_group)
      return reduce_buffer.reshape_as(self)
  
imports: import torch
  from dataclasses import dataclass
  from deepspeed import comm
  from typing import Dict
label_desc: Use image node_id 3 for calling a global function with example usage: get_full_hp_grad() and returns: reduce_buffer