Tensorflow share weights across input placeholder
A simple option would be to replicate W. If the original W is, lets say p X q, you can just do l = tf.matmul(X, tf.tile(W, (k, 1))).
I am trying to build a neural network with shared input weights. 
Given pk inputs in the form X=[x_1, ..., x_p, v_1,...,v_p,z1,...,z_p,...] and a weight matrix w  of shape (p, layer1_size),  I want the  the first layer to be defined as  sum(w, x_.) + sum(w, v_.) + ....
In other words the input and the first layer should be fully connected where weights are shared across the different groups of inputs.  l = tf.matmul(X, W) where each row of W must have a structure like: (w1, ... ,wp, w1, ..., wp, ...)
How can I do that in tensorflow?