code: def wordpieces(sequences):
      strings = []
      for token_lengths in sequences:
          strings.append([])
          for length in token_lengths:
              strings[-1].extend(str(i) for i in range(length))
      shape = (len(strings), max(len(seq) for seq in strings))
      wordpieces = WordpieceBatch(
          strings=strings,
          input_ids=numpy.zeros(shape, dtype="i"),
          token_type_ids=numpy.zeros(shape, dtype="i"),
          attention_mask=numpy.zeros(
              (shape[0], shape[1]), dtype="bool"
          ),
          lengths=[len(seq) for seq in strings],
      )
      return wordpieces
  
file_path: spacy-transformers/spacy_transformers/tests/test_truncation.py
imports: import pytest
  import numpy
  from thinc.types import Ragged
  from thinc.api import NumpyOps
  from ..data_classes import WordpieceBatch
  from ..truncate import _truncate_tokens, _truncate_alignment
label_desc: Use image node_id 4 for calling a global function with example usage: wordpieces(sequences) and returns: wordpieces