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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 _simulate_coef_from_bootstraps( self, n_draws, coef_bootstraps, cov_bootstraps):
 """Simulate coefficients using bootstrap samples."""

        # Sample indices uniformly from {0, ..., n_bootstraps - 1}
        # (Wood pg. 199 step 6)
        random_bootstrap_indices = np.random.choice(
            np.arange(len(coef_bootstraps)), size=n_draws, replace=True)

        # Simulate `n_draws` many random coefficient vectors from a
        # multivariate normal distribution with mean and covariance given by
        # the bootstrap samples (indexed by `random_bootstrap_indices`) of
        # `coef_bootstraps` and `cov_bootstraps`. Because it's faster to draw
        # many samples from a certain distribution all at once, we make a dict
        # mapping bootstrap indices to draw indices and use the `size`
        # parameter of `np.random.multivariate_normal` to sample the draws
        # needed from that bootstrap sample all at once.
        bootstrap_index_to_draw_indices = defaultdict(list)
        for draw_index, bootstrap_index in enumerate(random_bootstrap_indices):
            bootstrap_index_to_draw_indices[bootstrap_index].append(draw_index)

        coef_draws = np.empty((n_draws, len(self.coef_)))

        for bootstrap, draw_indices in bootstrap_index_to_draw_indices.items():
            coef_draws[draw_indices] = np.random.multivariate_normal(
                coef_bootstraps[bootstrap], cov_bootstraps[bootstrap],
                size=len(draw_indices))

        return coef_draws