code: def test_torchscript_intent_slot_output_layer(
      self, num_doc_labels, num_word_labels, seq_lens
  ):
      batch_size = len(seq_lens)
      doc_vocab = Vocabulary(
          [
              OutputLayerTest._generate_random_string()
              for _ in range(num_doc_labels)
          ]
      )
      word_vocab = Vocabulary(
          [
              OutputLayerTest._generate_random_string()
              for _ in range(num_word_labels)
          ]
      )
      intent_slot_output_layer = IntentSlotOutputLayer.from_config(
          config=IntentSlotOutputLayer.Config(),
          doc_labels=doc_vocab,
          word_labels=word_vocab,
      )
      doc_logits = OutputLayerTest._generate_doc_classification_inputs(
          batch_size, num_doc_labels
      )
      (
          word_logits,
          seq_lens_tensor,
      ) = OutputLayerTest._generate_word_tagging_inputs(
          batch_size, num_word_labels, seq_lens
      )
      context = {"seq_lens": seq_lens_tensor}
      torchscript_output_layer = (
          intent_slot_output_layer.torchscript_predictions()
      )
  
      pt_output = intent_slot_output_layer.get_pred(
          (doc_logits, word_logits), None, context
      )[1]
      with redirect_stdout() as redirected_stdout:
          ts_output = torchscript_output_layer(
              (doc_logits, word_logits), context
          )
          buffer = redirected_stdout.getvalue()
          assert (
              "Implicit dimension choice for log_softmax has been deprecated"
              not in buffer
          )
  
      self._validate_doc_classification_result(
          pt_output[0], ts_output[0], doc_vocab
      )
      self._validate_word_tagging_result(
          pt_output[1], ts_output[1], word_vocab
      )
  
      (
          word_bpe_logits,
          seq_lens_tensor,
          token_indices_tensor,
      ) = OutputLayerTest._generate_bpe_tagging_inputs(
          batch_size, num_word_labels, seq_lens
      )
      context = {
          "seq_lens": seq_lens_tensor,
          "token_indices": token_indices_tensor,
      }
      pt_output = intent_slot_output_layer.get_pred(
          (doc_logits, word_bpe_logits), None, context
      )[1]
      ts_output = torchscript_output_layer(
          (doc_logits, word_bpe_logits), context
      )
  
      self._validate_doc_classification_result(
          pt_output[0], ts_output[0], doc_vocab
      )
      self._validate_word_tagging_result(
          pt_output[1], ts_output[1], word_vocab
      )
  
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 4 for calling the OutputLayerTest obj's underlying member method code with example usage: obj.test_torchscript_intent_slot_output_layer(num_doc_labels, num_word_labels, seq_lens) without return types