code: def get_layer_timing_signal_learned_1d(channels, layer, num_layers):
    
    shape = [num_layers, 1, 1, channels]
    layer_embedding = (
        tf.get_variable(
            "layer_embedding",
            shape,
            initializer=tf.random_normal_initializer(0, channels**-0.5)) *
        (channels**0.5))
    return layer_embedding[layer, :, :, :]
docs: get n-dimensional embedding as the layer (vertical) timing signal.
  
    Adds embeddings to represent the position of the layer in the tower.
  
    Args:
      channels: dimension of the timing signal
      layer: layer num
      num_layers: total number of layers
  
    Returns:
      a Tensor of timing signals [1, 1, channels].