class_docstr: DocModel that receives both word IDs and byte IDs as inputs (concatenating
  word and byte-token embeddings to represent input tokens).
class_docstr_tok: ["DocModel","that","receives","both","word","IDs","and","byte","IDs","as","inputs","(","concatenating","word","and","byte-token","embeddings","to","represent","input","tokens",")","."]
code: def arrange_model_inputs(self, tensor_dict):
      tokens, seq_lens, _ = tensor_dict["tokens"]
      token_bytes, byte_seq_lens, _ = tensor_dict["token_bytes"]
      assert (seq_lens == byte_seq_lens).all().item()
      model_inputs = tokens, token_bytes, seq_lens
      if "dense" in tensor_dict:
          model_inputs += (tensor_dict["dense"],)
      return model_inputs
  
imports: from typing import Dict, List, Optional, Union
  import torch
  from pytext.config.component import create_loss
  from pytext.data.tensorizers import ByteTokenTensorizer, FloatListTensorizer, LabelTensorizer, NumericLabelTensorizer, Tensorizer, TokenTensorizer, UidTensorizer, VocabConfig
  from pytext.data.tokenizers import DoNothingTokenizer
  from pytext.exporters.exporter import ModelExporter
  from pytext.loss import BinaryCrossEntropyLoss, BinaryCrossEntropyWithLogitsLoss, MultiLabelSoftMarginLoss
  from pytext.models.decoders.mlp_decoder import DecoderBase, MLPDecoder
  from pytext.models.embeddings import CharacterEmbedding, EmbeddingBase, EmbeddingList, WordEmbedding
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from pytext.models.output_layers import ClassificationOutputLayer, OutputLayerBase, RegressionOutputLayer
  from pytext.models.output_layers.doc_classification_output_layer import BinaryClassificationOutputLayer, MulticlassOutputLayer, MultiLabelOutputLayer
  from pytext.models.representations.bilstm_doc_attention import BiLSTMDocAttention
  from pytext.models.representations.deepcnn import DeepCNNRepresentation
  from pytext.models.representations.docnn import DocNNRepresentation
  from pytext.models.representations.pure_doc_attention import PureDocAttention
  from pytext.models.representations.representation_base import RepresentationBase
  from pytext.torchscript.utils import make_byte_inputs, make_sequence_lengths, truncate_tokens
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytext.utils.label import get_label_weights
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.utils.transform_utils import pad_2d
  from torch import jit
label_desc: Use image node_id 2 for calling the ByteTokensDocumentModel obj's underlying member method code with example usage: obj.arrange_model_inputs(tensor_dict) and returns: model_inputs