code: def test_subsample_smoke(Model, t_obs, t_forecast, obs_dim, cov_dim):
      batch_shape = (4,)
      model = Model()
      data = torch.randn(batch_shape + (t_obs, obs_dim))
      covariates = torch.randn(
          batch_shape + (t_obs + t_forecast, cov_dim)
      )
  
      def create_plates(zero_data, covariates):
          size = len(zero_data)
          return pyro.plate("batch", size, subsample_size=2, dim=-2)
  
      forecaster = Forecaster(
          model,
          data,
          covariates[..., :t_obs, :],
          num_steps=2,
          log_every=1,
          create_plates=create_plates,
      )
  
      num_samples = 5
      samples = forecaster(data, covariates, num_samples)
      assert samples.shape == (num_samples,) + batch_shape + (
          t_forecast,
          obs_dim,
      )
      samples = forecaster(data, covariates, num_samples, batch_size=2)
      assert samples.shape == (num_samples,) + batch_shape + (
          t_forecast,
          obs_dim,
      )
  
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 4 for calling a global function with example usage: test_subsample_smoke(Model, t_obs, t_forecast, obs_dim, cov_dim) and returns: pyro