code: def compress_encoder_1d(x, hparams, name=None):
    
    x = tf.expand_dims(x, axis=2)
    return compress_encoder(x,
                            hparams,
                            strides=(2, 1),
                            kernel_size=(hparams.kernel_size, 1),
                            name=name)
docs: Encoder that compresses 1-D inputs by 2**num_compress_steps.
  
    Args:
      x: Tensor of shape [batch, length, channels].
      hparams: HParams.
      name: string, variable scope.
  
    Returns:
      Tensor of shape [batch, latent_length, hparams.hidden_size], where
        latent_length is
        hparams.num_latents * length / 2**hparams.num_compress_steps.