class_docstr: Transform molecules from a latent space feature vector into
  a one hot encoding.
  https://arxiv.org/abs/1610.02415
class_docstr_tok: ["Transform","molecules","from","a","latent","space","feature","vector","into","a","one","hot","encoding",".","https",":","\/\/arxiv.org\/abs\/1610.02415"]
docstr: Returns
  -------
  obj
    A Decoder with weights that were trained on the zinc dataset
docstr_tok: ["Returns","--","--","--","-","obj","A","Decoder","with","weights","that","were","trained","on","the","zinc","dataset"]
code: def zinc_decoder():
      """
      Returns
      -------
      obj
        A Decoder with weights that were trained on the zinc dataset
      """
      current_dir = os.path.dirname(os.path.realpath(__file__))
      weights_filename = "zinc_model.h5"
      weights_file = os.path.join(current_dir, weights_filename)
  
      if not os.path.exists(weights_file):
          download_url(
              "http://karlleswing.com/misc/keras-molecule/model.h5",
              current_dir,
          )
          mv_cmd = "mv model.h5 %s" % weights_file
          call(mv_cmd.split())
      return TensorflowMoleculeDecoder(
          model_dir=current_dir, weights_file=weights_filename
      )
  
file_path: deepchem/contrib/autoencoder_models/autoencoder.py
imports: import warnings
  from deepchem.models import Model
  from deepchem.models.autoencoder_models.model import MoleculeVAE
  from deepchem.feat.one_hot import zinc_charset
  from deepchem.utils import download_url
  import os
  from subprocess import call
label_desc: Use image node_id 3 for calling the TensorflowMoleculeDecoder obj's underlying member method code with example usage: obj.zinc_decoder() and returns: TensorflowMoleculeDecoder