code: def normal_normal_model(data):
      x = torch.tensor([0.0])
      y = pyro.sample("y", dist.Normal(x, torch.ones(data.shape)))
      pyro.sample("obs", dist.Normal(y, torch.tensor([1.0])), obs=data)
      return y
  
imports: import os
  from functools import partial
  import pytest
  import torch
  import pyro
  import pyro.distributions
  from pyro import poutine
  from pyro.infer.mcmc import HMC, NUTS
  from pyro.infer.mcmc.api import MCMC, StreamingMCMC, _MultiSampler, _UnarySampler
  from pyro.infer.mcmc.mcmc_kernel import MCMCKernel
  from pyro.infer.mcmc.util import initialize_model, select_samples
  from pyro.ops.streaming import StackStats, StatsOfDict
  from pyro.util import optional
  from tests.common import assert_close
label_desc: Use image node_id 1 for calling a global function with example usage: normal_normal_model(data) and returns: y