code: def test_for_partial_ent_sents():
      """Spans may be associated with multiple sentences. These .sents should always be complete, not partial, sentences,
      which this tests for.
      """
      doc = Doc(
          English().vocab,
          words=[
              "Mahler's",
              "Symphony",
              "No.",
              "8",
              "was",
              "beautiful.",
          ],
          sent_starts=[1, 0, 0, 1, 0, 0],
      )
      doc.set_ents([Span(doc, 1, 4, "WORK")])
      # The specified entity is associated with both sentences in this doc, so we expect all sentences in the doc to be
      # equal to the sentences referenced in ent.sents.
      for doc_sent, ent_sent in zip(doc.sents, doc.ents[0].sents):
          assert doc_sent == ent_sent
  
imports: import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from thinc.api import get_current_ops
  from spacy.attrs import LENGTH, ORTH
  from spacy.lang.en import English
  from spacy.tokens import Doc, Span, Token
  from spacy.util import filter_spans
  from spacy.vocab import Vocab
  from ..util import add_vecs_to_vocab
  from .test_underscore import clean_underscore
label_desc: Use image node_id 42 for calling a global function with example usage: test_for_partial_ent_sents() without return types