title: How to perform reduce_op on multiple dimensions at once?
body: <p>Let's say I have a list of rank N tensors expressed as a rank N+1 tensor. For example, a list of 100 10x20 matrices as a rank 3 tensor with shape (100,10,20). I need to perform the same operations on each of those matrices: summing all the elements, mean and median of the values of the ith matrix, for each matrix.
  Is it possible to do something like <code>tf.math.reduce_sum</code>, <code>tf.math.reduce_mean</code>, <code>tf.contrib.distributions.percentile</code>, along axis 0, but computing the whole matrix at once for each element along the 0 axis?</p>
  
  <p>E.G.: </p>
  
  <pre><code>matricesList = tf.constant([[[1,1],[1,1]],
                             [[2,2],[2,2]]])
  
  op = sum_matrices_along_axis(matrixList)
  </code></pre>
  
  <p>With expected <code>op = [4,8]</code></p>