class_docstr: Single Sentence Token-level Classification Model using XLM.
class_docstr_tok: ["Single","Sentence","Token-level","Classification","Model","using","XLM","."]
code: def arrange_model_inputs(self, tensor_dict):
      tokens, pad_mask, segment_labels, positions, _ = tensor_dict[
          "tokens"
      ]
      model_inputs = (tokens, pad_mask, segment_labels, positions)
      return (model_inputs,)
  
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import torch
  from pytext import resources
  from pytext.common.constants import Stage, TORCH_VERSION
  from pytext.config import ConfigBase
  from pytext.data.roberta_tensorizer import RoBERTaTensorizer, RoBERTaTokenLevelTensorizer
  from pytext.data.tensorizers import FloatListTensorizer, LabelTensorizer, NumericLabelTensorizer, Tensorizer
  from pytext.models.bert_classification_models import NewBertModel
  from pytext.models.bert_regression_model import NewBertRegressionModel
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.model import BaseModel
  from pytext.models.module import create_module, Module
  from pytext.models.output_layers import WordTaggingOutputLayer
  from pytext.models.representations.transformer import MultiheadLinearAttention, PassthroughEncoder, PassthroughTransformer, PostEncoder, QuantizedMultiheadLinearAttention, SELFIETransformer, SentenceEncoder
  from pytext.models.representations.transformer_sentence_encoder_base import PoolingMethod, TransformerSentenceEncoderBase
  from pytext.models.utils import normalize_embeddings
  from pytext.torchscript.module import ScriptPyTextEmbeddingModuleIndex, ScriptPyTextModule, ScriptPyTextModuleWithDense
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.nn.modules.multihead_attention import MultiheadSelfAttention
  from pytorch.text.fb.nn.modules.transformer import DEFAULT_MAX_SEQUENCE_LENGTH, Transformer, TransformerLayer
  from torch import nn
  from torch.serialization import default_restore_location
  from .r3f_models import R3FConfigOptions, R3FPyTextMixin
label_desc: Use image node_id 3 for calling the RoBERTaWordTaggingModel obj's underlying member method code with example usage: obj.arrange_model_inputs(tensor_dict) and returns: model_inputs