code: def _get_seq_nn_rand_input(
      self, batch_size, w_vocab_size, num_words, num_seq=1
  ):
      seq = torch.from_numpy(
          np.random.randint(
              w_vocab_size, size=(batch_size, num_seq, num_words)
          ).astype(np.int64)
      )
      seq_lengths = torch.from_numpy(
          np.random.randint(
              num_seq, num_seq + 1, size=(batch_size)
          ).astype(np.int64)
      )
      return (seq, seq_lengths)
  
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 9 for calling the ModelExporterTest obj's underlying member method code with example usage: obj._get_seq_nn_rand_input(batch_size, w_vocab_size, num_words, num_seq) and returns: seq,  seq_lengths