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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 prior_rvs(self, size=1, prior=None):
 """Returns random variates drawn from the prior. If the ``sampling_params`` are different from the ``variable_params``, the variates are transformed to the `sampling_params` parameter space before being returned. Parameters size : int, optional Number of random values to return for each parameter. Default is 1. prior : JointDistribution, optional Use the given prior to draw values rather than the saved prior. Returns ------- FieldArray A field array of the random values. """

        # draw values from the prior
        if prior is None:
            prior = self.prior_distribution
        p0 = prior.rvs(size=size)
        # transform if necessary
        if self.sampling_transforms is not None:
            ptrans = self.sampling_transforms.apply(p0)
            # pull out the sampling args
            p0 = FieldArray.from_arrays([ptrans[arg]
                                         for arg in self.sampling_params],
                                        names=self.sampling_params)
        return p0