code: def _test_task_export_to_caffe2(self, task_class, config):
      task = task_class.from_config(config.task)
      py_model = task.model
      with tempfile.NamedTemporaryFile(
          delete=False, suffix=".{}".format("predictor")
      ) as pred_file:
          print(pred_file.name)
          output_names = task.export(py_model, pred_file.name)
          print(output_names)
          workspace.ResetWorkspace()
  
      pred_net = pe.prepare_prediction_net(
          pred_file.name, CAFFE2_DB_TYPE
      )
  
      test_inputs = py_model.arrange_model_inputs(
          next(iter(task.data.batches(Stage.TEST)))[1]
      )
      ModelExporterTest._feed_c2_input(
          workspace,
          test_inputs,
          # get_export_input_names only implemented for document classification
          task.model.get_export_input_names(task.data.tensorizers),
          task.model.vocab_to_export(task.data.tensorizers),
      )
      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 softmax since we do log softmax before exporting predictor nets
      # We do exp on caffe2 output instead, because log of numbers that are
      # very close to 0 gives different result in pytorch and caffe2
      py_outs = F.softmax(py_outs, 1)
  
      np.testing.assert_array_almost_equal(
          py_outs.view(-1).detach().numpy(),
          np.exp(np.array(c2_out).transpose()).flatten(),
      )
  
imports: import tempfile
  import unittest
  from typing import Type
  import caffe2.python.hypothesis_test_util
  import caffe2.python.predictor.predictor_exporter
  import numpy
  import torch.nn.functional
  from caffe2.python import workspace
  from pytext.common.constants import Stage
  from pytext.config import ExportConfig, LATEST_VERSION, PyTextConfig
  from pytext.data import Data, PoolingBatcher
  from pytext.data.sources import TSVDataSource
  from pytext.data.tensorizers import FloatListTensorizer
  from pytext.models.doc_model import DocModel, PersonalizedDocModel
  from pytext.models.model import Model
  from pytext.task.new_task import NewTask
  from pytext.task.tasks import DocumentClassificationTask
  from pytext.trainers import TaskTrainer
  from pytext.utils.onnx import CAFFE2_DB_TYPE
  from pytext.utils.test import import_tests_module
label_desc: Use image node_id 4 for calling the ModelExporterTest obj's underlying member method code with example usage: obj._test_task_export_to_caffe2(task_class, config) without return types