def __init__(self, latent_size, hidden_size):
    """Constructs an encoder for `f`.

    Args:
      latent_size: An integer corresponding to the dimensionality of the
        distribution.
      hidden_size: Dimensionality of the LSTM, RNN, and affine function
        parameters.
    """
    super(EncoderStatic, self).__init__()
    self.latent_size = latent_size
    self.hidden_size = hidden_size
    self.bilstm = tf.keras.layers.Bidirectional(
        tf.keras.layers.LSTM(hidden_size),
        merge_mode="sum")
    self.output_layer = tf.keras.layers.Dense(2*latent_size)
def __init__ ( self , latent_size , hidden_size ) : super ( EncoderStatic , self ) . __init__ ( ) self . latent_size = latent_size self . hidden_size = hidden_size self . bilstm = tf . keras . layers . Bidirectional ( tf . keras . layers . LSTM ( hidden_size ) , merge_mode = "sum" ) self . output_layer = tf . keras . layers . Dense ( 2 * latent_size )
Constructs an encoder for `f`.

    Args:
      latent_size: An integer corresponding to the dimensionality of the
        distribution.
      hidden_size: Dimensionality of the LSTM, RNN, and affine function
        parameters.