code: def add_depth_embedding(x):
    
    x_shape = common_layers.shape_list(x)
    depth = x_shape[-1]
    num_steps = x_shape[0]
    shape = [num_steps, 1, 1, depth]
    depth_embedding = (
        tf.get_variable(
            "depth_embedding",
            shape,
            initializer=tf.random_normal_initializer(0, depth**-0.5)) * (depth**
                                                                         0.5))
  
    x += depth_embedding
    return x
docs: Add n-dimensional embedding as the depth embedding (timing signal).
  
    Adds embeddings to represent the position of the step in the recurrent
    tower.
  
    Args:
      x: a tensor with shape [max_step, batch, length, depth]
  
    Returns:
      a Tensor the same shape as x.