code: def test_wordblstm_export_to_caffe2(
      self,
      export_num_words,
      num_word_classes,
      test_num_words,
      num_predictions,
  ):
      for WORD_CONFIG in WORD_CONFIGS:
          config = self._get_config(WordTaggingTask.Config, WORD_CONFIG)
          tensorizers, data = _NewTask._init_tensorizers(config)
          word_labels = [
              SpecialTokens.PAD,
              SpecialTokens.UNK,
              "NoLabel",
              "person",
          ]
          tensorizers["labels"].vocab = Vocabulary(word_labels)
          tensorizers["tokens"].vocab = Vocabulary(WORD_VOCAB)
          py_model = _NewTask._init_model(config.model, tensorizers)
          dummy_test_input = self._get_rand_input_intent_slot(
              BATCH_SIZE, W_VOCAB_SIZE, test_num_words
          )
          exporter = ModelExporter(
              ModelExporter.Config(),
              py_model.get_export_input_names(tensorizers),
              dummy_test_input,
              py_model.vocab_to_export(tensorizers),
              py_model.get_export_output_names(tensorizers),
          )
          with tempfile.NamedTemporaryFile(
              delete=False, suffix=".{}".format(".predictor")
          ) as pred_file:
              exporter.export_to_caffe2(py_model, pred_file.name)
              workspace.ResetWorkspace()
          pred_net = pe.prepare_prediction_net(
              pred_file.name, CAFFE2_DB_TYPE
          )
          for _i in range(num_predictions):
              test_inputs = self._get_rand_input_intent_slot(
                  BATCH_SIZE, W_VOCAB_SIZE, test_num_words
              )
              self._feed_c2_input(
                  workspace,
                  test_inputs,
                  exporter.input_names,
                  exporter.vocab_map,
              )
              workspace.RunNetOnce(pred_net)
              word_output_names = [
                  "{}:{}".format("word_scores", class_name)
                  for class_name in word_labels
              ]
              py_model.eval()
              py_outs = py_model(*test_inputs)
              context = {"seq_lens": test_inputs[-1]}
              target = None
              pred, score = py_model.get_pred(py_outs, target, context)
              c2_word_out = []
              for o_name in word_output_names:
                  c2_word_out.extend(list(workspace.FetchBlob(o_name)))
  
              np.testing.assert_array_almost_equal(
                  torch.transpose(score, 1, 2)
                  .contiguous()
                  .view(-1)
                  .detach()
                  .numpy(),
                  np.array(c2_word_out).flatten(),
              )
  
imports: import json
  import operator
  import tempfile
  from collections import Counter
  from distutils.version import LooseVersion
  import caffe2.python.hypothesis_test_util
  import caffe2.python.predictor.predictor_exporter
  import hypothesis.strategies
  import numpy
  import torch
  import torch.nn.functional
  import torchtext
  from caffe2.python import workspace
  from hypothesis import given, settings
  from pytext.builtin_task import DocumentClassificationTask, IntentSlotTask, SeqNNTask, WordTaggingTask
  from pytext.common.constants import DatasetFieldName, SpecialTokens
  from pytext.config import config_from_json
  from pytext.config.component import create_exporter, create_model
  from pytext.data import CommonMetadata
  from pytext.data.utils import Vocabulary
  from pytext.exporters.exporter import ModelExporter
  from pytext.fields import CharFeatureField, DictFeatureField, FieldMeta, SeqFeatureField, TextFeatureField
  from pytext.legacy.vocab import Vocab
  from pytext.task.new_task import _NewTask
  from pytext.utils.onnx import CAFFE2_DB_TYPE
label_desc: Use image node_id 3 for calling the ModelExporterTest obj's underlying member method code with example usage: obj.test_wordblstm_export_to_caffe2(export_num_words, num_word_classes, test_num_words, num_predictions) without return types