def sample_prob(self, probs, rand):
    ' takes a tensor of probabilitiesas from a sigmoidal activation and sample from all \n        the distributions. \n        probs INPUT parameter: tensor of probabilities \n        rand INPUT parameter :tensor (of same shape as probabilities) of random values \n        :RETURN binary sample of probabilities \n        '
    return tf.nn.relu(tf.sign((probs - rand)))
takes a tensor of probabilitiesas from a sigmoidal activation and sample from all 
the distributions. 
probs INPUT parameter: tensor of probabilities 
rand INPUT parameter :tensor (of same shape as probabilities) of random values 
:RETURN binary sample of probabilities
def sample_prob(self, probs, rand):
    ' takes a tensor of probabilitiesas from a sigmoidal activation and sample from all \n        the distributions. \n        probs INPUT parameter: tensor of probabilities \n        rand INPUT parameter :tensor (of same shape as probabilities) of random values \n        :RETURN binary sample of probabilities \n        '
    return tf.nn.relu(tf.sign((probs - rand)))