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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 likelihood(self, outcomes, modelparams, expparams):
 """ Calculates the likelihood function at the states specified by modelparams and measurement specified by expparams. This is given by the Born rule and is the probability of outcomes given the state and measurement operator. """

        
        # By calling the superclass implementation, we can consolidate
        # call counting there.
        super(MultiQubitStatePauliModel, self).likelihood(outcomes, modelparams, expparams)
        
        
        # Note that expparams['axis'] has shape (n_exp, 3).
        pr0 = 0.5*(1 + modelparams[:,expparams['pauli']])

        # Use the following hack if you don't want to ensure positive weights
        pr0[pr0 < 0] = 0
        pr0[pr0 > 1] = 1
        
        # Note that expparams['vis'] has shape (n_exp, ).
        pr0 = expparams['vis'] * pr0 + (1 - expparams['vis']) * 0.5

        # Now we concatenate over outcomes.
        return Model.pr0_to_likelihood_array(outcomes, pr0)