code: def test_predict(component, docs):
      trf_data = component.predict(docs)
      n_tokens = trf_data.wordpieces.input_ids.shape[1]
      width = component.model.layers[0].attrs["width"]
      assert isinstance(trf_data, FullTransformerBatch)
      assert (
          len(trf_data.model_output.last_hidden_state)
          == component.model.layers[0].attrs["depth"]
      )
      assert trf_data.model_output.last_hidden_state[0].shape == (
          len(docs),
          n_tokens,
          width,
      )
  
imports: import pytest
  from packaging.version import Version
  import torch
  import spacy
  from spacy.language import Language
  from spacy.training.example import Example
  from spacy.util import make_tempdir
  from spacy.vocab import Vocab
  from spacy.tokens import Doc
  from spacy import util
  from thinc.api import Model, Config, get_current_ops, NumpyOps
  from spacy.tests.util import assert_docs_equal
  from .util import DummyTransformer, _assert_equal_tensors
  from ..None import TransformerModel
  from ..pipeline_component import Transformer
  from ..layers import TransformerListener
  from ..data_classes import TransformerData, FullTransformerBatch
label_desc: Use image node_id 6 for calling a global function with example usage: test_predict(component, docs) without return types