class_docstr: TokenizerModelAdapter moves input onto device and adds batch dimension
class_docstr_tok: ["TokenizerModelAdapter","moves","input","onto","device","and","adds","batch","dimension"]
code: def __init__(self, padding_idx):
      super().__init__()
      self._padding_idx = padding_idx
      self._dummy_param = nn.Parameter(torch.empty(0))
  
file_path: serve/examples/text_classification_with_scriptable_tokenizer/script_tokenizer_and_model.py
imports: import argparse
  from typing import Any
  import torch
  import torchtext.functional
  import torchtext.transforms
  from torch import nn
  from torch.hub import load_state_dict_from_url
  from torchtext.models import XLMR_BASE_ENCODER, RobertaClassificationHead
label_desc: Use image node_id 1 to create a new TokenizerModelAdapter object from inherited base classes: nn with example: obj = TokenizerModelAdapter(padding_idx)