local

    self.bypass_bias_init_consts = bypass_bias_init_consts

    self.bypass_dropouts = bypass_dropouts

  def build(self, graph, name_scopes, training):

    """Constructs the graph architecture as specified in its config.

    n_layers = len(layer_sizes)

    assert n_layers == len(bypass_layer_sizes)

    if not isinstance(weight_init_stddevs, collections.Sequence):

      weight_init_stddevs = [weight_init_stddevs] * n_layers

    if not isinstance(bypass_weight_init_stddevs, collections.Sequence):

      bypass_weight_init_stddevs = [bypass_weight_init_stddevs] * n_layers

    if not isinstance(bias_init_consts, collections.Sequence):

      bias_init_consts = [bias_init_consts] * n_layers

    if not isinstance(dropouts, collections.Sequence):

      dropouts = [dropouts] * n_layers

    if not isinstance(activation_fns, collections.Sequence):

      activation_fns = [activation_fns] * n_layers

    # Add the input features.

    mol_features = Feature(shape=(None, n_features))

    prev_layer = mol_features

    This method creates the following Placeholders:

      mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape

        batch_size x num_features.

    """

    num_features = self.n_features

    placeholder_scope = TensorflowGraph.get_placeholder_scope(

        graph, name_scopes)

    with graph.as_default():

      with placeholder_scope:

        mol_features = tf.placeholder(

            tf.float32, shape=[None, num_features], name='mol_features')

      layer_sizes = self.layer_sizes

      weight_init_stddevs = self.weight_init_stddevs