class_docstr: Base test class and used for CPU case.
code: def _get_transformer(self, seed=0):
      torch.manual_seed(seed)  # keep everything deterministic
      module = torch.nn.Transformer(
          d_model=32,
          num_encoder_layers=2,
          num_decoder_layers=2,
          dim_feedforward=128,
          dropout=0.1,
      )
      module.register_buffer("dummy_buffer", torch.tensor(1.0))
  
      def get_input(device, dtype):
          torch.manual_seed(1)  # keep everything deterministic
          src = torch.rand(20, 8, 32).to(
              device=device, dtype=dtype
          )  # T x B x C
          tgt = torch.rand(10, 8, 32).to(
              device=device, dtype=dtype
          )  # T x B x C
          return (src, tgt)
  
      module.get_input = get_input
      module.param_list = None  # No param_list to FPW.
      module.flat_param_names = None  # No flat_param_names to FPW.
      return module
  
imports: from collections import OrderedDict
  import unittest
  import torch
  from fairscale.fair_dev.testing.testing import objects_are_equal
  from fairscale.nn import FlattenParamsWrapper
label_desc: Use image node_id 3 for calling the TestFlattenParams obj's underlying member method code with example usage: obj._get_transformer(seed) and returns: module, src,  tgt