code: def model(self, zero_data, covariates):
      with pyro.plate_stack(
          "batch", zero_data.shape[:-2], rightmost_dim=-2
      ):
          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)
  
          scale_tril = torch.eye(zero_data.size(-1))
          noise_dist = dist.MultivariateNormal(
              zero_data, scale_tril=scale_tril
          )
          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 Model2 obj's underlying member method code with example usage: obj.model(zero_data, covariates) without return types