code: def neg_log_likelihood(self, x):
          
          x_reshape = tf.reshape(x, [self.batch_size, self.eq_samples, self.iw_samples, self.num_latent])
          density = tf.nn.sigmoid_cross_entropy_with_logits(self.mu, x_reshape)
          # sum over all importance weights. average over all eq_samples
          return tf.reduce_mean(tf.reduce_sum(-density, axis=2), axis=(1, 2))
docs: Calculate Log Likelihood with particular mean and std
          x must be 2D. [batch_size * eqsamples* iwsamples, num_latent]