code: def _get_rand_input(
      self,
      features,
      batch_size,
      w_vocab_size,
      d_vocab_size,
      c_vocab_size,
      num_words,
      num_dict_feats,
      num_chars,
      num_seq=1,
  ):
      text = torch.from_numpy(
          np.random.randint(
              w_vocab_size, size=(batch_size, num_words)
          ).astype(np.int64)
      )
      lengths = torch.from_numpy(
          np.random.randint(
              num_words, num_words + 1, size=(batch_size)
          ).astype(np.int64)
      )
      dict_feat = torch.from_numpy(
          np.random.randint(
              d_vocab_size,
              size=(batch_size, num_dict_feats * num_words),
          ).astype(np.int64)
      )
      dict_weights = torch.from_numpy(
          np.random.randn(
              batch_size, num_words * num_dict_feats
          ).astype(np.float32)
      )
      dict_lengths = torch.from_numpy(
          np.random.randint(
              1, num_dict_feats + 1, size=(batch_size, num_words)
          ).astype(np.int64)
      )
      chars = torch.from_numpy(
          np.random.randint(
              c_vocab_size, size=(batch_size, num_words, num_chars)
          ).astype(np.int64)
      )
      dense_features = torch.from_numpy(
          np.random.rand(batch_size, DENSE_FEATURE_DIM).astype(
              np.float32
          )
      )
      inputs = []
      if features.word_feat:
          inputs.append(text)
      if features.dict_feat:
          inputs.append((dict_feat, dict_weights, dict_lengths))
      if features.char_feat:
          inputs.append(chars)
      if getattr(features, "seq_word_feat", False):
          inputs.append(
              torch.from_numpy(
                  np.random.randint(
                      w_vocab_size,
                      size=(batch_size, num_seq, num_words),
                  ).astype(np.int64)
              )
          )
      inputs.append(lengths)
      if getattr(features, "seq_word_feat", False):
          inputs.append(
              torch.from_numpy(
                  np.random.randint(
                      num_seq, num_seq + 1, size=(batch_size)
                  ).astype(np.int64)
              )
          )
      if features.dense_feat:
          inputs.append(dense_features)
      return tuple(inputs)
  
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 10 for calling the ModelExporterTest obj's underlying member method code with example usage: obj._get_rand_input(features, batch_size, w_vocab_size, d_vocab_size, c_vocab_size, num_words, num_dict_feats, num_chars, num_seq) and returns: tuple