code: def _generate_word_tagging_inputs(
      bsize: int, num_labels: int, seq_lens: List[int]
  ):
      max_seq_length = max(seq_lens)
      logits = torch.randn(bsize, max_seq_length, num_labels)
      return logits, torch.tensor(seq_lens, dtype=torch.int)
  
imports: import random
  import string
  import sys
  from contextlib import contextmanager
  from io import StringIO
  from typing import Dict, List
  import caffe2.python.hypothesis_test_util
  import hypothesis.strategies
  import numpy
  import torch
  from hypothesis import given, settings
  from pytext.common.constants import SpecialTokens
  from pytext.data.tensorizers import LabelTensorizer
  from pytext.data.utils import Vocabulary
  from pytext.loss import CrossEntropyLoss
  from pytext.models.output_layers.doc_classification_output_layer import ClassificationOutputLayer
  from pytext.models.output_layers.intent_slot_output_layer import IntentSlotOutputLayer
  from pytext.models.output_layers.word_tagging_output_layer import CRFOutputLayer, WordTaggingOutputLayer
label_desc: Use image node_id 6 for calling the OutputLayerTest obj's underlying member method code with example usage: obj._generate_word_tagging_inputs(bsize, num_labels, seq_lens) and returns: logits,  torch