code: def test_pyrocov_reparam(model, Guide, backend):
      T, P, S, F = 2, 3, 4, 5
      dataset = {
          "features": torch.randn(S, F),
          "local_time": torch.randn(T, P),
          "weekly_strains": torch.randn(T, P, S).exp().round(),
      }
  
      # Reparametrize the model.
      config = {
          "coef": LocScaleReparam(),
          "rate_loc": None
          if model is pyrocov_model
          else LocScaleReparam(),
          "rate": LocScaleReparam(),
          "init_loc": LocScaleReparam(),
          "init": LocScaleReparam(),
      }
      model = poutine.reparam(model, config)
      guide = Guide(model, backend=backend)
      svi = SVI(model, guide, ClippedAdam({"lr": 1e-8}), Trace_ELBO())
      for step in range(2):
          with xfail_if_not_implemented():
              svi.step(dataset)
      guide(dataset)
      predictive = Predictive(model, guide=guide, num_samples=2)
      predictive(dataset)
  
imports: from collections import OrderedDict, namedtuple
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.infer import SVI, JitTrace_ELBO, Predictive, Trace_ELBO
  from pyro.infer.autoguide import AutoGaussian, AutoGuideList
  from pyro.infer.autoguide.gaussian import AutoGaussianDense, AutoGaussianFunsor, _break_plates
  from pyro.infer.reparam import LocScaleReparam
  from pyro.optim import ClippedAdam
  from tests.common import assert_close, assert_equal, xfail_if_not_implemented
label_desc: Use image node_id 21 for calling a global function with example usage: test_pyrocov_reparam(model, Guide, backend) without return types