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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 _extract_log_likelihood(self):
 """Compute log likelihood of each observation. Return None if there is not exactly 1 observed random variable. """

        # This next line is brittle and may not work forever, but is a secret
        # way to access the model from the trace.
        model = self.trace._straces[0].model  # pylint: disable=protected-access
        if len(model.observed_RVs) != 1:
            return None, None
        else:
            if self.dims is not None:
                coord_name = self.dims.get(model.observed_RVs[0].name)
            else:
                coord_name = None

        cached = [(var, var.logp_elemwise) for var in model.observed_RVs]

        def log_likelihood_vals_point(point):
            """Compute log likelihood for each observed point."""
            log_like_vals = []
            for var, log_like in cached:
                log_like_val = log_like(point)
                if var.missing_values:
                    log_like_val = log_like_val[~var.observations.mask]
                log_like_vals.append(log_like_val.ravel())
            return np.concatenate(log_like_vals)

        chain_likelihoods = []
        for chain in self.trace.chains:
            log_like = (log_likelihood_vals_point(point) for point in self.trace.points([chain]))
            chain_likelihoods.append(np.stack(log_like))
        return np.stack(chain_likelihoods), coord_name