code: def model(self, zero_data, covariates):
      with pyro.plate("batch", len(zero_data), dim=-2):
          zero_data = pyro.subsample(zero_data, event_dim=1)
          covariates = pyro.subsample(covariates, event_dim=1)
  
          loc = zero_data[..., :1, :]
          scale = pyro.sample(
              "scale", dist.LogNormal(loc, 1).to_event(1)
          )
  
          with self.time_plate:
              jumps = pyro.sample(
                  "jumps", dist.Normal(0, scale).to_event(1)
              )
          prediction = jumps.cumsum(-2)
  
          duration, obs_dim = zero_data.shape[-2:]
          noise_dist = dist.LinearHMM(
              dist.Stable(1.9, 0).expand([obs_dim]).to_event(1),
              torch.eye(obs_dim),
              dist.Stable(1.9, 0).expand([obs_dim]).to_event(1),
              torch.eye(obs_dim),
              dist.Stable(1.9, 0).expand([obs_dim]).to_event(1),
              duration=duration,
          )
          rep = StableReparam()
          with poutine.reparam(
              config={"residual": LinearHMMReparam(rep, rep, rep)}
          ):
              self.predict(noise_dist, prediction)
  
imports: import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.contrib.forecast import Forecaster, ForecastingModel, HMCForecaster
  from pyro.infer.autoguide import AutoDelta
  from pyro.infer.reparam import LinearHMMReparam, StableReparam
  from pyro.optim import Adam
label_desc: Use image node_id 1 for calling the SubsampleModel4 obj's underlying member method code with example usage: obj.model(zero_data, covariates) without return types