Project: s41467-024-44803-4 | Function: _tokenize (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Custom tokenization function for SARS-CoV-2 mutations that preprocesses mutation notation and splits by whitespace to create tokens for BERT model

**KEY TERMS:** {
  "text": "mutation sequence",
  "tokens": "mutation words",
  ":": "mutation position separator",
  "-": "deletion indicator"
}

**CONTEXT CODE:** import os
import json

from transformers import BertTokenizer, BertModel
from torch import nn
from typing import List, Dict, Optional, Union, Tuple

# CustomTransformer is the same implementation of
# https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/models/Transformer.py
# but with a custom tokenizer and custom model loading


class CustomTokenizer(BertTokenizer):
    def _tokenize(self, text):
        # MASKED: Custom tokenization function for SARS-CoV-2 mutations implementation
        raise NotImplementedError("This function is masked for testing purposes.")

class CustomTransformer(nn.Module):
    """
    changed implementation of the sentence transformers model
    """
    def __init__(self, model_name_or_path: str, max_seq_length: Optional[int] = None,
                 model_args: Dict = {}, cache_dir: Optional[str] = None,
                 tokenizer_args: Dict = {}, do_lower_case: bool = False,
                 tokenizer_name_or_path : str = None):
        super(CustomTransformer, self).__init__()
        self.config_keys = ['max_seq_length', 'do_lower_case']
        self.do_lower_case = do_lower_case

        self.auto_model = BertModel.from_pretrained('/sternadi/home/volume3/chronic-corona-pred/sars_cov_2_mlm/models/pretrained-covBERTa/checkpoint-29000/',
                                 output_hidden_states = True)

        self.tokenizer = CustomTokenizer.from_pretrained('/sternadi/home/volume3/chronic-corona-pred/sars_cov_2_mlm/models/pretrained-covBERTa/',
                                            do_lower_case=False)

        #No max_seq_length set. Try to infer from model
        if max_seq_length is None:
            if hasattr(self.auto_model, "config") and hasattr(self.auto_model.config, "max_position_embeddings") \
                    and hasattr(self.tokenizer, "model_max_length"):
                max_seq_length = min(self.auto_model.config.max_position_embeddings, self.tokenizer.model_max_length)

        self.max_seq_length = max_seq_length

        if tokenizer_name_or_path is not None:
            self.auto_model.config.tokenizer_class = self.tokenizer.__class__.__name__


    def __repr__(self):
        return "Transformer({}) with Transformer model: {} ".format(self.get_config_dict(), self.auto_model.__class__.__name__)

    def forward(self, features):
        """Returns token_embeddings, cls_token"""
        # MASKED: Forward pass through BERT model for mutation sequence embeddings implementation
        raise NotImplementedError("This function is masked for testing purposes.")

    def get_word_embedding_dimension(self) -> int:
        return self.auto_model.config.hidden_size

    def tokenize(self, texts: Union[List[str], List[Dict], List[Tuple[str, str]]]):
        """
        Tokenizes a text and maps tokens to token-ids
        """
        output = {}
        if isinstance(texts[0], str):
            to_tokenize = [texts]
        elif isinstance(texts[0], dict):
            to_tokenize = []
            output['text_keys'] = []
            for lookup in texts:
                text_key, text = next(iter(lookup.items()))
                to_tokenize.append(text)
                output['text_keys'].append(text_key)
            to_tokenize = [to_tokenize]
        else:
            batch1, batch2 = [], []
            for text_tuple in texts:
                batch1.append(text_tuple[0])
                batch2.append(text_tuple[1])
            to_tokenize = [batch1, batch2]

        #strip
        to_tokenize = [[str(s).strip() for s in col] for col in to_tokenize]

        #Lowercase
        if self.do_lower_case:
            to_tokenize = [[s.lower() for s in col] for col in to_tokenize]

        output.update(self.tokenizer(*to_tokenize, padding=True, truncation='longest_first', return_tensors="pt", max_length=self.max_seq_length))
        return output


    def get_config_dict(self):
        return {key: self.__dict__[key] for key in self.config_keys}

    def save(self, output_path: str):
        self.auto_model.save_pretrained(output_path)
        self.tokenizer.save_pretrained(output_path)

        with open(os.path.join(output_path, 'sentence_bert_config.json'), 'w') as fOut:
            json.dump(self.get_config_dict(), fOut, indent=2)



Complete the function implementation replacing "raise NotImplementedError".