There are many methods that expect a list or a tensor specifying a shape as argument. It is often the case that I want to pass an argument such as shape=[t1, v2] where t1 is a tensor with shape = TensorShape([]) and v2 is a value. This is currently not possible in many (maybe all) methods.

My current work around is the following.
1- convert v2 to a tensor t2 with tf.convert_to_tensor
2- use t1 = tf.expand_dims(0, t1) and t2 = tf.expand_dims(0, t2) to add a dimension of size 1 to the tensors
3- pass the argument as shape=tf.concat(0, [t1, t2])

Am I missing a simpler way to accomplish that? Is it a good idea to add a feature that allows passing mixture of tensors and values inside a list as argument?

Edit: I just found out that the method tf.pack allows lists with tensors and values as argument. But the method tf.truncated_normal for instance, doesn't allow.
I also realized that I can use tf.pack to replace the steps (1,2,3) above. But it still would be nice if all methods had the same behaviour.