__call__transformers.PythonBackend.__call__https://github.com/huggingface/transformers/blob/v5.6.2/src/transformers/tokenization_utils_base.py#L2415[{"name": "text", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "text_pair", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "text_target", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "text_pair_target", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "add_special_tokens", "val": ": bool = True"}, {"name": "padding", "val": ": bool | str | PaddingStrategy = False"}, {"name": "truncation", "val": ": bool | str | TruncationStrategy | None = None"}, {"name": "max_length", "val": ": int | None = None"}, {"name": "stride", "val": ": int = 0"}, {"name": "is_split_into_words", "val": ": bool = False"}, {"name": "pad_to_multiple_of", "val": ": int | None = None"}, {"name": "padding_side", "val": ": str | None = None"}, {"name": "return_tensors", "val": ": str | TensorType | None = None"}, {"name": "return_token_type_ids", "val": ": bool | None = None"}, {"name": "return_attention_mask", "val": ": bool | None = None"}, {"name": "return_overflowing_tokens", "val": ": bool = False"}, {"name": "return_special_tokens_mask", "val": ": bool = False"}, {"name": "return_offsets_mapping", "val": ": bool = False"}, {"name": "return_length", "val": ": bool = False"}, {"name": "verbose", "val": ": bool = True"}, {"name": "tokenizer_kwargs", "val": ": dict[str, Any] | None = None"}, {"name": "**kwargs", "val": ""}]- **text** (`str`, `list[str]`, `list[list[str]]`, *optional*) --
  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
  (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
  `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
- **text_pair** (`str`, `list[str]`, `list[list[str]]`, *optional*) --
  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
  (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
  `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
- **text_target** (`str`, `list[str]`, `list[list[str]]`, *optional*) --
  The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
  list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
  you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
- **text_pair_target** (`str`, `list[str]`, `list[list[str]]`, *optional*) --
  The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
  list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
  you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
- **tokenizer_kwargs** (`dict[str, Any]`, *optional*) --
  Additional kwargs to pass to the tokenizer. These will be merged with the explicit parameters and
  other kwargs, with explicit parameters taking precedence.