human
Here's a good function header given the following:
I wrote the below code

    def norm_fn(x, name):
        with tf.variable_scope(name, default_name='norm'):
            return common_layers.apply_norm(x, hparams.norm_type, hparams.hidden_size, hparams.norm_epsilon)
    targets_flat = tf.expand_dims(common_layers.flatten4d3d(targets), axis=2)
    target_space_emb = tf.tile(target_space_emb, [tf.shape(targets_flat)[0], 1, 1, 1])
    if ((len(hparams.problems) > 1) and (hparams.sim_loss_mult > 1e-05)):
        targets_timed = common_layers.add_timing_signal(targets_flat)
        extra_layers = int((hparams.num_hidden_layers * 1.5))
        with tf.variable_scope(tf.get_variable_scope(), reuse=True):
            targets_encoded = multi_conv_res(targets_timed, 'SAME', 'encoder', extra_layers, hparams)
        with tf.variable_scope('similarity_loss'):
            similarity_loss = similarity_cost(inputs_encoded, targets_encoded)
            similarity_loss *= hparams.sim_loss_mult
    else:
        similarity_loss = 0.0
    targets_shifted = common_layers.shift_left(targets_flat, pad_value=target_space_emb)
    if (hparams.attention_type == 'none'):
        targets_with_attention = tf.zeros_like(targets_shifted)
    else:
        inputs_padding_bias = ((1.0 - mask) * (-1000000000.0))
        targets_with_attention = attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=inputs_padding_bias)
    kernel = (hparams.kernel_height, hparams.kernel_width)
    targets_merged = common_layers.subseparable_conv_block(tf.concat([targets_with_attention, targets_shifted], axis=3), hparams.hidden_size, [((1, 1), kernel)], normalizer_fn=norm_fn, padding='LEFT', separability=4, name='targets_merge')
    return (targets_merged, similarity_loss)

What's a good function header?
assistant
#Here's a good function header.

def slicenet_middle(inputs_encoded, targets, target_space_emb, mask, hparams):