code: def __init__(
      self,
      input_texts: List,
      ys: Optional[List],
      keywords: List[str],
      batch_size: int,
      tokenizer: PreTrainedTokenizer,
      tokenizer_args: Dict,
  ):
      self._input_texts = input_texts
      self._ys = ys
      self._bs = batch_size
      self._keys = keywords
      self._tokenizer = tokenizer
      if self._tokenizer.pad_token is None:
          self._tokenizer.pad_token = self._tokenizer.eos_token
      _tokenizer_args = {"truncation": True, "padding": True}
      _tokenizer_args.update(tokenizer_args)
      self._tokenizer_args = _tokenizer_args
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/conversions/huggingface.py
imports: from typing import List, Dict, Sequence, Optional
  import numpy
  from nebullvm.core.models import Device
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch
  from nebullvm.optional_modules.huggingface import PreTrainedTokenizer, PreTrainedModel
  from nebullvm.tools.huggingface import get_output_structure_from_dict, get_output_structure_from_text, PyTorchTransformerWrapper, TensorFlowTransformerWrapper
  from nebullvm.tools.utils import is_dict_type
label_desc: Use image node_id 1 to create a new _HFTextDataset object from inherited base classes: Sequence with example: obj = _HFTextDataset(input_texts, ys, keywords, batch_size, tokenizer, tokenizer_args)