code: def _feed_c2_input(workspace, py_inputs, input_names, vocab_map):
      c2_input = []
      for py_input in py_inputs:
          c2_input = c2_input + (
              list(py_input)
              if isinstance(py_input, tuple)
              else [py_input]
          )
      for i, input in enumerate(list(c2_input)):
          input_np = input.numpy()
          if input_names[i] in vocab_map.keys():
              # Map the input to the str form
              input_vocab = vocab_map[input_names[i]]
              map_fn = np.vectorize(lambda x: input_vocab[x])
              input_str = map_fn(input_np)
              input_np = np.array(input_str, dtype=str)
              workspace.FeedBlob(
                  input_names[i] + "_str:value", input_np
              )
          else:
              workspace.FeedBlob(input_names[i], input_np)
  
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 1 for calling the ModelExporterTest obj's underlying member method code with example usage: obj._feed_c2_input(workspace, py_inputs, input_names, vocab_map) without return types