code: def model(self, zero_data, covariates):
      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)
  
      noise_dist = dist.Normal(zero_data, 1)
      self.predict(noise_dist, prediction)
  
file_path: pyro/tests/contrib/forecast/test_evaluate.py
imports: import math
  import pytest
  import torch
  import pyro
  import pyro.distributions
  from pyro.contrib.forecast import Forecaster, ForecastingModel, HMCForecaster, backtest
  from pyro.contrib.forecast.evaluate import DEFAULT_METRICS
  from pyro.util import optional
label_desc: Use image node_id 1 for calling the Model obj's underlying member method code with example usage: obj.model(zero_data, covariates) without return types