code: def _generate_bpe_tagging_inputs(
      bsize: int, num_labels: int, seq_lens: List[int]
  ):
      max_seq_length = max(seq_lens)
      max_bpe_length = max_seq_length * 2 + random.randint(
          1, max_seq_length
      )  # Arbitrary length greater than max_seq_length
      logits = torch.randn(bsize, max_bpe_length, num_labels)
      token_begin_indices = []
      for l in seq_lens:
          token_begin_indices.append(
              sorted(random.sample(range(max_bpe_length), l))
              + [0] * (max_seq_length - l)
          )
      return (
          logits,
          torch.tensor(seq_lens, dtype=torch.int),
          torch.tensor(token_begin_indices, dtype=torch.long),
      )
  
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 7 for calling the OutputLayerTest obj's underlying member method code with example usage: obj._generate_bpe_tagging_inputs(bsize, num_labels, seq_lens) and returns: logits,  torch,  torch