code: def test_spancat_multiprocessing(name, n_process):
      if isinstance(get_current_ops, NumpyOps) or n_process < 2:
          nlp = Language()
          spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
          train_examples = make_examples(nlp)
          nlp.initialize(get_examples=lambda: train_examples)
          texts = [
              "Just a sentence.",
              "I like London and Berlin",
              "I like Berlin",
              "I eat ham.",
          ]
          docs = list(nlp.pipe(texts, n_process=n_process))
          assert len(docs) == len(texts)
  
imports: import numpy
  import pytest
  from numpy.testing import assert_almost_equal, assert_array_equal
  from thinc.api import NumpyOps, Ragged, get_current_ops
  from spacy import util
  from spacy.lang.en import English
  from spacy.language import Language
  from spacy.tokens import SpanGroup
  from spacy.tokens._dict_proxies import SpanGroups
  from spacy.training import Example
  from spacy.util import fix_random_seed, make_tempdir, registry
label_desc: Use image node_id 17 for calling a global function with example usage: test_spancat_multiprocessing(name, n_process) without return types