code: def _buildEncoder(
      self, x, latent_rep_size, max_length, epsilon_std=0.01
  ):
      h = Convolution1D(9, 9, activation="relu", name="conv_1")(x)
      h = Convolution1D(9, 9, activation="relu", name="conv_2")(h)
      h = Convolution1D(10, 11, activation="relu", name="conv_3")(h)
      h = Flatten(name="flatten_1")(h)
      h = Dense(435, activation="relu", name="dense_1")(h)
  
      def sampling(args):
          z_mean_, z_log_var_ = args
          batch_size = K.shape(z_mean_)[0]
          epsilon = K.random_normal(
              shape=(batch_size, latent_rep_size),
              mean=0.0,
              std=epsilon_std,
          )
          return z_mean_ + K.exp(z_log_var_ / 2) * epsilon
  
      z_mean = Dense(
          latent_rep_size, name="z_mean", activation="linear"
      )(h)
      z_log_var = Dense(
          latent_rep_size, name="z_log_var", activation="linear"
      )(h)
  
      def vae_loss(x, x_decoded_mean):
          x = K.flatten(x)
          x_decoded_mean = K.flatten(x_decoded_mean)
          xent_loss = max_length * objectives.binary_crossentropy(
              x, x_decoded_mean
          )
          kl_loss = -0.5 * K.mean(
              1 + z_log_var - K.square(z_mean) - K.exp(z_log_var),
              axis=-1,
          )
          return xent_loss + kl_loss
  
      return (
          vae_loss,
          Lambda(
              sampling, output_shape=(latent_rep_size,), name="lambda"
          )([z_mean, z_log_var]),
      )
  
file_path: deepchem/contrib/autoencoder_models/model.py
imports: import warnings
  from keras import backend
  from keras import objectives
  from keras.layers import Input, Lambda
  from keras.layers.convolutional import Convolution1D
  from keras.layers.core import Dense, Flatten, RepeatVector
  from keras.layers.recurrent import GRU
  from keras.layers.wrappers import TimeDistributed
  from keras.models import Model
label_desc: Use image node_id 3 for calling the MoleculeVAE obj's underlying member method code with example usage: obj._buildEncoder(x, latent_rep_size, max_length, epsilon_std) and returns: vae_loss,  Lambda, unknown, unknown