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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_loss_func(self, C=1.0, k=1):
 """Get loss function of VAE. The loss value is equal to ELBO (Evidence Lower Bound) multiplied by -1. Args: C (int):
 Usually this is 1.0. Can be changed to control the second term of ELBO bound, which works as regularization. k (int):
 Number of Monte Carlo samples used in encoded vector. """

        def lf(x):
            mu, ln_var = self.encode(x)
            batchsize = len(mu.data)
            # reconstruction loss
            rec_loss = 0
            for l in six.moves.range(k):
                z = F.gaussian(mu, ln_var)
                rec_loss += F.bernoulli_nll(x, self.decode(z, sigmoid=False)) \
                    / (k * batchsize)
            self.rec_loss = rec_loss
            self.loss = self.rec_loss + \
                C * gaussian_kl_divergence(mu, ln_var) / batchsize
            return self.loss
        return lf