code: def do_optim(model, optimizer, N, rank):
      torch.random.manual_seed(rank)
      for i in range(N):
          optimizer.zero_grad()
          NE = model.weight.shape[0]
          inputs = (torch.rand(10) * NE).long()
          L = model(inputs).sum()
          L.backward()
          # print(next(model.parameters()).grad)
          optimizer.step()
  
imports: import logging
  import os
  import unittest
  from unittest import main, TestCase
  import torch
  import torch.multiprocessing
  import torch.nn
  from torch.optim import Adagrad
  from torchbiggraph.async_adagrad import AsyncAdagrad
  from torchbiggraph.row_adagrad import RowAdagrad
label_desc: Use image node_id 1 for calling a global function with example usage: do_optim(model, optimizer, N, rank) without return types