class_docstr: Run basic SMILES tokenization using a regex pattern developed by Schwaller et. al.
  This tokenizer is to be used when a tokenizer that does not require the transformers library by HuggingFace is required.
  
  Examples
  --------
  >>> from deepchem.feat.smiles_tokenizer import BasicSmilesTokenizer
  >>> tokenizer = BasicSmilesTokenizer()
  >>> print(tokenizer.tokenize("CC(=O)OC1=CC=CC=C1C(=O)O"))
  ['C', 'C', '(', '=', 'O', ')', 'O', 'C', '1', '=', 'C', 'C', '=', 'C', 'C', '=', 'C', '1', 'C', '(', '=', 'O', ')', 'O']
  
  
  References
  ----------
  .. [1] Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee
      ACS Central Science 2019 5 (9): Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
      1572-1583 DOI: 10.1021/acscentsci.9b00576
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docstr: Constructs a BasicSMILESTokenizer.
  
  Parameters
  ----------
  regex: string
      SMILES token regex
docstr_tok: ["Constructs","a","BasicSMILESTokenizer",".","Parameters","--","--","--","--","--","regex",":","string","SMILES","token","regex"]
code: def __init__(self, regex_pattern: str = SMI_REGEX_PATTERN):
      """Constructs a BasicSMILESTokenizer.
  
      Parameters
      ----------
      regex: string
          SMILES token regex
      """
      self.regex_pattern = regex_pattern
      self.regex = re.compile(self.regex_pattern)
  
imports: import collections
  import os
  import re
  import pkg_resources
  from typing import List, Optional
  from transformers import BertTokenizer
  from logging import getLogger
label_desc: Use image node_id 1 to create a new BasicSmilesTokenizer object from inherited base classes: object with example: obj = BasicSmilesTokenizer(regex_pattern)