code: def __init__(
      self,
      data,
      type: Optional[FeatureType] = None,
      try_type: Optional[FeatureType] = None,
      col: Optional[str] = None,
      optimized_int_type: Optional[FeatureType] = None,
  ):
      optimized_int_type_by_col = {
          "attention_mask": Value("int8"),  # binary tensor
          "special_tokens_mask": Value("int8"),
          "input_ids": Value(
              "int32"
          ),  # typical vocab size: 0-50k (max ~500k, never > 1M)
          "token_type_ids": Value(
              "int8"
          ),  # binary mask; some (XLNetModel) use an additional token represented by a 2
      }
      if type is None and try_type is None:
          optimized_int_type = optimized_int_type_by_col.get(col, None)
      super().__init__(
          data,
          type=type,
          try_type=try_type,
          optimized_int_type=optimized_int_type,
      )
  
imports: import errno
  import json
  import os
  import sys
  from pathlib import Path
  from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  import fsspec
  import numpy
  import pyarrow
  import pyarrow.parquet
  from .None import config
  from .features import Features, Image, Value
  from .features.features import FeatureType, _ArrayXDExtensionType, cast_to_python_objects, generate_from_arrow_type, get_nested_type, list_of_np_array_to_pyarrow_listarray, numpy_to_pyarrow_listarray, to_pyarrow_listarray
  from .filesystems import is_remote_filesystem
  from .info import DatasetInfo
  from .keyhash import DuplicatedKeysError, KeyHasher
  from .table import array_cast, array_concat, cast_array_to_feature, embed_table_storage, table_cast
  from .utils import logging
  from .utils import tqdm
  from .utils.file_utils import hash_url_to_filename
  from .utils.py_utils import asdict, first_non_null_value
label_desc: Use image node_id 1 to create a new OptimizedTypedSequence object from inherited base classes: TypedSequence with example: obj = OptimizedTypedSequence(data, type, try_type, col, optimized_int_type)