class_docstr: Also a word tagging model, but uses bytes as inputs to the model. Using
  bytes instead of words, the model does not need to store a word embedding
  table mapping words in the vocab to their embedding vector representations,
  but instead compute them on the fly using CharacterEmbedding. This produces
  an exported/serialized model that requires much less storage space as well
  as less memory during run/inference time.
code: def get_export_input_names(self, tensorizers):
      return ["token_bytes", "token_lens"]
  
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 3 for calling the WordTaggingLiteModel obj's underlying member method code with example usage: obj.get_export_input_names(tensorizers) and returns: list