class_docstr: All the following tests do not check for inter-process communication
code: def test_state_dict(self):
      x = torch.tensor([1.0], device=DEVICE, requires_grad=True)
      o = optim.OSS([x], lr=0.1, momentum=0.9)
      x.backward()
      o.step()
      assert x == torch.tensor([0.9], device=DEVICE)
      assert o.optim.state[x]["momentum_buffer"] == torch.tensor(
          [1.0], device=DEVICE
      )
      o.zero_grad()
      o.consolidate_state_dict()  # Sync state dict in between replicas - even if there are none
      state_dict = o.state_dict()
  
      # Check that the state dict is pytorch-compliant key wise
      assert "param_groups" in state_dict.keys()
      assert "state" in state_dict.keys()
  
      # Check that the pulled state is what we expect, and that we have all the expected keys
      assert state_dict["param_groups"][0]["lr"] == 0.1
      assert state_dict["param_groups"][0]["momentum"] == 0.9
      assert not state_dict["param_groups"][0]["nesterov"]
      assert state_dict["param_groups"][0]["weight_decay"] == 0.0
      assert state_dict["param_groups"][0]["dampening"] == 0.0
  
      # Check that the pulled state and the .param_groups attribute are in sync
      for k in state_dict["param_groups"][0].keys():
          if k != "params":
              assert (
                  state_dict["param_groups"][0][k]
                  == o.param_groups[0][k]
              )
  
      # Check that it's correctly loaded
      o = optim.OSS([x], lr=0.01)
      o.load_state_dict(state_dict)
      # Check that state is correct and on proper device
      assert o.optim.state[x]["momentum_buffer"] == torch.tensor(
          [1.0], device=DEVICE
      )
  
      # We should now be using a lr of 0.1, both within the optimizer
      # and as exposed by the .param_groups attribute
      assert o.param_groups[0]["lr"] == 0.1
      x.backward()
      o.step()
      assert x == torch.tensor([0.71], device=DEVICE)
      assert o.optim.state[x]["momentum_buffer"] == torch.tensor(
          [1.9], device=DEVICE
      )
  
      # Check that the exposed param_groups are on the proper device
      assert o.param_groups[0]["params"][0].device == x.device
  
imports: import copy
  from math import inf
  import tempfile
  from typing import Any, Dict, Type, cast
  import unittest
  import numpy
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from fairscale.fair_dev.testing.testing import check_same_model_params, check_same_models_across_ranks, skip_if_no_cuda, skip_if_py39_no_cuda, skip_if_single_gpu
  from fairscale.internal import torch_version
  import fairscale.optim
label_desc: Use image node_id 4 for calling the TestSingleRank obj's underlying member method code with example usage: obj.test_state_dict() without return types