code: def test_create_word_tagging_output_layer(self):
      tensorizer = LabelTensorizer()
      tensorizer.vocab = Vocabulary(["foo", "bar"])
      tensorizer.pad_idx = 0
      layer = WordTaggingOutputLayer.from_config(
          config=WordTaggingOutputLayer.Config(
              label_weights={"foo": 2.2}
          ),
          labels=tensorizer.vocab,
      )
      np.testing.assert_array_almost_equal(
          np.array([2.2, 1]), layer.loss_fn.weight.detach().numpy()
      )
  
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 2 for calling the OutputLayerTest obj's underlying member method code with example usage: obj.test_create_word_tagging_output_layer() without return types