class_docstr: Word tagging model. It can be used for any task that requires predicting the
  tag for a word/token. For example, the following tasks can be modeled as word
  tagging tasks. This is not an exhaustive list.
  1. Part of speech tagging.
  2. Named entity recognition.
  3. Slot filling for task oriented dialog.
  
  It can be instantiated just like any other :class:`~Model`.
code: def arrange_targets(self, tensor_dict):
      return tensor_dict["labels"]
  
imports: from typing import List, Optional, Union
  import torch
  from pytext.common.constants import SpecialTokens
  from pytext.data.tensorizers import ByteTokenTensorizer, SlotLabelTensorizer, TokenTensorizer
  from pytext.data.tokenizers import DoNothingTokenizer
  from pytext.exporters.exporter import ModelExporter
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.embeddings import CharacterEmbedding, WordEmbedding
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from pytext.models.output_layers import CRFOutputLayer, WordTaggingOutputLayer
  from pytext.models.representations.bilstm_slot_attn import BiLSTMSlotAttention
  from pytext.models.representations.biseqcnn import BSeqCNNRepresentation
  from pytext.models.representations.deepcnn import DeepCNNRepresentation
  from pytext.models.representations.pass_through import PassThroughRepresentation
  from pytext.torchscript.utils import make_byte_inputs, make_sequence_lengths, truncate_tokens
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.utils.transform_utils import pad_2d
label_desc: Use image node_id 5 for calling the WordTaggingModel obj's underlying member method code with example usage: obj.arrange_targets(tensor_dict) and returns: tensor_dict