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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 _sample_points(X, centers, oversampling_factor, random_state):
 r""" Sample points independently with probability .. math:: p_x = \frac{\ell \cdot d^2(x, \mathcal{C})}{\phi_X(\mathcal{C})} """

    # re-implement evaluate_cost here, to avoid redundant computation
    distances = pairwise_distances(X, centers).min(1) ** 2
    denom = distances.sum()
    p = oversampling_factor * distances / denom

    draws = random_state.uniform(size=len(p), chunks=p.chunks)
    picked = p > draws

    new_idxs, = da.where(picked)
    return new_idxs