code: def nested_iplate_model(subsample_size):
      loc = torch.zeros(20)
      scale = torch.ones(20)
      result = []
      inner_iplate = pyro.plate("inner", 20, 5)
      for i in pyro.plate("outer", 20, subsample_size):
          result.append([])
          for j in inner_iplate:
              pyro.sample(
                  "x_{}_{}".format(i, j),
                  dist.Normal(loc[i] + loc[j], scale[i] + scale[j]),
              )
              result[-1].append(j)
      return result
  
file_path: pyro/tests/poutine/test_mapdata.py
imports: import logging
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from tests.common import requires_cuda
label_desc: Use image node_id 4 for calling a global function with example usage: nested_iplate_model(subsample_size) and returns: result