class_docstr: Base test class and used for CPU case.
code: def _get_pnorm_after_step(self, module):
      optim = torch.optim.SGD(module.parameters(), lr=0.01)
      loss = self._compute_output(module).sum()
      loss.backward()
      optim.step()
      return torch.norm(
          torch.stack([p.detach().norm() for p in module.parameters()])
      )
  
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 9 for calling the TestFlattenParams obj's underlying member method code with example usage: obj._get_pnorm_after_step(module) and returns: torch