code: def neg_log_likelihood(self, x, eps=1e-10):
          
          x_reshape = tf.reshape(x, [self.batch_size, self.eq_samples, self.iw_samples, self.num_latent])
  
          # mean over the softmax outputs inside the log domain.
          pi = tf.reduce_mean(self.pi, axis=(1, 2))
  
          density = tf.reduce_mean(x * tf.log(pi + eps))
          return tf.reduce_sum(-density, axis=2)
docs: Calculate Log Likelihood with particular mean and std
          x must be 2D. [batch_size * eqsamples* iwsamples, num_latent]