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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 iwls(y, x, family, offset, y_fix, ini_betas=None, tol=1.0e-8, max_iter=200, wi=None):
 """ Iteratively re-weighted least squares estimation routine Parameters y : array n*1, dependent variable x : array n*k, designs matrix of k independent variables family : family object probability models: Gaussian, Poisson, or Binomial offset : array n*1, the offset variable for each observation. y_fix : array n*1, the fixed intercept value of y for each observation ini_betas : array 1*k, starting values for the k betas within the iteratively weighted least squares routine tol : float tolerance for estimation convergence max_iter : integer maximum number of iterations if convergence not met wi : array n*1, weights to transform observations from location i in GWR Returns ------- betas : array k*1, estimated coefficients mu : array n*1, predicted y values wx : array n*1, final weights used for iwls for GLM n_iter : integer number of iterations that when iwls algorithm terminates w : array n*1, final weights used for iwls for GWR z : array iwls throughput v : array iwls throughput xtx_inv_xt : array iwls throughout to compute GWR hat matrix [X'X]^-1 X' """

    n_iter = 0
    diff = 1.0e6

    if ini_betas is None:
        betas = np.zeros((x.shape[1], 1), np.float)
    else:
        betas = ini_betas

    if isinstance(family, Binomial):
        y = family.link._clean(y)
    if isinstance(family, Poisson):
        y_off = y / offset
        y_off = family.starting_mu(y_off)
        v = family.predict(y_off)
        mu = family.starting_mu(y)
    else:
        mu = family.starting_mu(y)
        v = family.predict(mu)

    while diff > tol and n_iter < max_iter:
        n_iter += 1
        w = family.weights(mu)
        z = v + (family.link.deriv(mu) * (y - mu))
        w = np.sqrt(w)
        if not isinstance(x, np.ndarray):
            w = sp.csr_matrix(w)
            z = sp.csr_matrix(z)
        wx = spmultiply(x, w, array_out=False)
        wz = spmultiply(z, w, array_out=False)
        if wi is None:
            n_betas = _compute_betas(wz, wx)
        else:
            n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
        v = spdot(x, n_betas)
        mu = family.fitted(v)

        if isinstance(family, Poisson):
            mu = mu * offset

        diff = min(abs(n_betas - betas))
        betas = n_betas

    if wi is None:
        return betas, mu, wx, n_iter
    else:
        return betas, mu, v, w, z, xtx_inv_xt, n_iter