I'm feeding in a dynamic shaped Tensor using: 

`x = tf.placeholder(tf.int32, shape=[None, vector_size])`

I need to turn this into a list of Tensors that have `shape=[1, vector_size]` using `x_list = tf.unpack(x, 0)`

But it raises a `ValueError` because the length of the first dimension is not known i.e. it's `None`. 

I've been trying to get around this by using another `tf.placeholder` to dynamically supply the shape of `x` but the parameter `shape` cannot be a Tensor.

How can I use `tf.unpack()` in this situation? 

Or is there another function that can also turn the variable that I feed in into a list of Tensors?

Thanks in advance.