code: def __getitem__(self, item: int):
      pointer = self._bs * item
      if pointer >= len(self._input_texts):
          raise IndexError
      mini_batch = self._input_texts[
          pointer : pointer + self._bs  # noqa E203
      ]
      if self._ys is not None:
          mini_batch_y = self._ys[
              pointer : pointer + self._bs
          ]  # noqa E203
      else:
          mini_batch_y = None
      encoded_inputs = self._tokenizer(
          mini_batch, **self._tokenizer_args
      )
      return (
          tuple(encoded_inputs[key] for key in self._keys),
          mini_batch_y,
      )
  
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 2 for calling the _HFTextDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: tuple,  mini_batch_y