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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 resample(self,N,minval=None,maxval=None,log=False,res=1e4):
 """Returns random samples generated according to the distribution Mirrors basic functionality of `rvs` method for `scipy.stats` random variates. Implemented by mapping uniform numbers onto the inverse CDF using a closest-matching grid approach. Parameters N : int Number of samples to return minval,maxval : float, optional Minimum/maximum values to resample. Should both usually just be `None`, which will default to `self.minval`/`self.maxval`. log : bool, optional Whether grid should be log- or linear-spaced. res : int, optional Resolution of CDF grid used. Returns ------- values : ndarray N samples. Raises ------ ValueError If maxval/minval are +/- infinity, this doesn't work because of the grid-based approach. """

        N = int(N)
        if minval is None:
            if hasattr(self,'minval_cdf'):
                minval = self.minval_cdf
            else:
                minval = self.minval
        if maxval is None:
            if hasattr(self,'maxval_cdf'):
                maxval = self.maxval_cdf
            else:
                maxval = self.maxval

        if maxval==np.inf or minval==-np.inf:
            raise ValueError('must have finite upper and lower bounds to resample. (set minval, maxval kws)')

        u = rand.random(size=N)
        if log:
            vals = np.logspace(log10(minval),log10(maxval),res)
        else:
            vals = np.linspace(minval,maxval,res)
            
        #sometimes cdf is flat.  so ys will need to be uniqued
        ys,yinds = np.unique(self.cdf(vals), return_index=True)
        vals = vals[yinds]
        

        inds = np.digitize(u,ys)
        return vals[inds]