code: def __call__(
      self,
      texts,
      add_special_tokens=True,
      max_length=None,
      stride: int = 0,
      truncation_strategy="longest_first",
      padding=False,
      truncation=False,
      is_pretokenized=False,
      return_tensors=None,
      return_token_type_ids=None,
      return_attention_mask=None,
      return_overflowing_tokens=False,
      return_special_tokens_masks=False,
      return_offsets_mapping=False,
      return_length=False,
  ):
      output: Dict = {
          "input_ids": [],
          "attention_mask": [],
          "token_type_ids": [],
      }  # type: ignore
  
      for text in texts:
          words, offsets, mask, type_ids = self._tokenize(text)
          ids = self._encode_words(words)
          output["input_ids"].append(ids)
          output["attention_mask"].append(mask)
          output["token_type_ids"].append(type_ids)
      if padding:
          output = self._pad(output)
      if return_tensors == "pt":
          output["input_ids"] = torch.tensor(output["input_ids"])  # type: ignore
          output["attention_mask"] = torch.tensor(output["attention_mask"])  # type: ignore
          output["token_type_ids"] = torch.tensor(output["token_type_ids"])  # type: ignore
      elif return_tensors == "np":
          output["input_ids"] = numpy.asarray(output["input_ids"])  # type: ignore
          output["attention_mask"] = numpy.asarray(output["attention_mask"])  # type: ignore
          output["token_type_ids"] = numpy.asarray(output["token_type_ids"])  # type: ignore
      if return_length:
          output["length"] = torch.tensor([len(x) for x in output["input_ids"]])  # type: ignore
      return output
  
imports: from typing import Dict, List, Union
  import numpy
  import torch
  import copy
  from transformers.file_utils import ModelOutput
  from numpy.testing import assert_array_equal
  from spacy.tokens import Doc
  from thinc.api import Model, get_current_ops
  from ..data_classes import FullTransformerBatch, HFObjects
  from ..span_getters import get_doc_spans
  from ..layers.transformer_model import forward
label_desc: Use image node_id 3 for calling the DummyTokenizer obj's underlying member method code with example usage: obj.__call__(texts, add_special_tokens, max_length, stride, truncation_strategy, padding, truncation, is_pretokenized, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_masks, return_offsets_mapping, return_length) and returns: output