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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 _build_likelihood(self):
 r""" Construct a tensorflow function to compute the likelihood. \log p(Y | theta). """

        K = self.kern.K(self.X) + tf.eye(tf.shape(self.X)[0], dtype=settings.float_type) * self.likelihood.variance
        L = tf.cholesky(K)
        m = self.mean_function(self.X)
        logpdf = multivariate_normal(self.Y, m, L)  # (R,) log-likelihoods for each independent dimension of Y

        return tf.reduce_sum(logpdf)