class_docstr: SmilesToSeq Featurizer takes a SMILES string, and turns it into a sequence.
  Details taken from [1]_.
  
  SMILES strings smaller than a specified max length (max_len) are padded using
  the PAD token while those larger than the max length are not considered. Based
  on the paper, there is also the option to add extra padding (pad_len) on both
  sides of the string after length normalization. Using a character to index (char_to_idx)
  mapping, the SMILES characters are turned into indices and the
  resulting sequence of indices serves as the input for an embedding layer.
  
  References
  ----------
  .. [1] Goh, Garrett B., et al. "Using rule-based labels for weak supervised
      learning: a ChemNet for transferable chemical property prediction."
      Proceedings of the 24th ACM SIGKDD International Conference on Knowledge
      Discovery & Data Mining. 2018.
  
  Note
  ----
  This class requires RDKit to be installed.
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docstr: Reconstructs SMILES string from sequence.
docstr_tok: ["Reconstructs","SMILES","string","from","sequence","."]
code: def smiles_from_seq(self, seq: List[int]) -> str:
      """Reconstructs SMILES string from sequence."""
      characters = [self.idx_to_char[i] for i in seq]
  
      characters = self.remove_pad(characters)
      smile = "".join([letter for letter in characters])
      return smile
  
file_path: deepchem/deepchem/feat/molecule_featurizers/smiles_to_seq.py
imports: from typing import Dict, List
  import numpy
  import pandas
  from deepchem.utils.typing import RDKitMol
  from deepchem.feat.base_classes import MolecularFeaturizer
label_desc: Use image node_id 4 for calling the SmilesToSeq obj's underlying member method code with example usage: obj.smiles_from_seq(seq) and returns: smile