code: def test_seq_nn_export_to_caffe2(
      self,
      export_num_words,
      num_doc_classes,
      test_num_words,
      num_predictions,
      test_num_seq,
  ):
      config = self._get_config(SeqNNTask.Config, SEQ_NN_CONFIG)
      tensorizers, data = _NewTask._init_tensorizers(config)
      doc_labels = [SpecialTokens.UNK, "cu:other", "cu:address_Person"]
      tensorizers["labels"].vocab = Vocabulary(doc_labels)
      tensorizers["tokens"].vocab = Vocabulary(WORD_VOCAB)
      py_model = _NewTask._init_model(config.model, tensorizers)
      dummy_test_input = self._get_seq_nn_rand_input(
          BATCH_SIZE, W_VOCAB_SIZE, test_num_words, test_num_seq
      )
      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:
          output_names = 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_seq_nn_rand_input(
              BATCH_SIZE, W_VOCAB_SIZE, test_num_words, test_num_seq
          )
          self._feed_c2_input(
              workspace,
              test_inputs,
              exporter.input_names,
              exporter.vocab_map,
          )
          workspace.RunNetOnce(pred_net)
          c2_out = [
              list(workspace.FetchBlob(o_name))
              for o_name in output_names
          ]
  
          py_model.eval()
          py_outs = py_model(*test_inputs)
          # Do log_softmax since we do that before exporting predictor nets
          py_outs = F.log_softmax(py_outs, 1)
          np.testing.assert_array_almost_equal(
              py_outs.view(-1).detach().numpy(),
              np.array(c2_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 6 for calling the ModelExporterTest obj's underlying member method code with example usage: obj.test_seq_nn_export_to_caffe2(export_num_words, num_doc_classes, test_num_words, num_predictions, test_num_seq) without return types