class_docstr: Transform molecules from one hot encoding into a latent vector
  representation.
  https://arxiv.org/abs/1610.02415
class_docstr_tok: ["Transform","molecules","from","one","hot","encoding","into","a","latent","vector","representation",".","https",":","\/\/arxiv.org\/abs\/1610.02415"]
code: def predict_on_batch(self, X):
      x_latent = self.model.encoder.predict(X)
      return x_latent
  
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 4 for calling the TensorflowMoleculeEncoder obj's underlying member method code with example usage: obj.predict_on_batch(X) and returns: x_latent