code: def test_scalar_save_variable_batch_size():
      x = nn.Variable((128, 1, 28, 28))
      h = PF.convolution(x, 32, kernel=(3, 3))
      loss = F.mean(h)
      with create_temp_with_dir("tmp.nnp") as temp_nnp_file_name:
          runtime_contents = {
              "networks": [
                  {
                      "name": "runtime",
                      "batch_size": 1,
                      "outputs": {"loss": loss},
                      "names": {"x": x},
                  }
              ],
              "executors": [
                  {
                      "name": "runtime",
                      "network": "runtime",
                      "data": ["x"],
                      "output": ["loss"],
                  }
              ],
          }
          nn.utils.save.save(temp_nnp_file_name, runtime_contents)
  
imports: import pytest
  import numpy
  import nnabla
  import nnabla.functions
  import nnabla.parametric_functions
  from nnabla.utils import load
  from nnabla.utils import save
  from nnabla.utils import nnp_graph
  from helper import forward_variable, create_temp_with_dir
label_desc: Use image node_id 16 for calling a global function with example usage: test_scalar_save_variable_batch_size() without return types