# shape: C x F/2
# output = self.permutations: [num_copies x cell_size]
permutations = []
indices = numpy.arange(self._dim / 2) #[1 ,2 ,3 ...64]
for i in range(self._num_copies):
    numpy.random.shuffle(indices) #[4, 48, 32, ...64]
    permutations.append(numpy.concatenate(
        [indices,
         [ind + self._dim / 2 for ind in indices]]))
    #you're appending a row with two columns -- a permutation in the first column, and the same permutation + dim/2 for imaginary
# C x F (numpy)
self.permutations = tf.constant(numpy.vstack(permutations), dtype = tf.int32) #This is a permutation tensor that has the stored permutations
# output = self.permutations: [num_copies x cell_size]

def permute(complex_tensor): #complex tensor is [batch_size x cell_size]
 gather_tensor = tf.gather_nd(complex_tensor, self.permutations)
 return gather_tensor

def permute(self, complex_tensor):
 inputs_permuted = []
 for i in range(self.permutations.get_shape()[0].value):
  inputs_permuted.append(
    tf.gather(complex_tensor, self.permutations[i]))
 return tf.concat(0, inputs_permuted)