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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 trustworthiness(X, X_embedded, n_neighbors=5, precomputed=False):
 """Expresses to what extent the local structure is retained. The trustworthiness is within [0, 1]. It is defined as .. math:: T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1} \sum_{j \in U^{(k)}_i} (r(i, j) - k) where :math:`r(i, j)` is the rank of the embedded datapoint j according to the pairwise distances between the embedded datapoints, :math:`U^{(k)}_i` is the set of points that are in the k nearest neighbors in the embedded space but not in the original space. * "Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study" J. Venna, S. Kaski * "Learning a Parametric Embedding by Preserving Local Structure" L.J.P. van der Maaten Parameters X : array, shape (n_samples, n_features) or (n_samples, n_samples) If the metric is 'precomputed' X must be a square distance matrix. Otherwise it contains a sample per row. X_embedded : array, shape (n_samples, n_components) Embedding of the training data in low-dimensional space. n_neighbors : int, optional (default: 5) Number of neighbors k that will be considered. precomputed : bool, optional (default: False) Set this flag if X is a precomputed square distance matrix. Returns ------- trustworthiness : float Trustworthiness of the low-dimensional embedding. """

    if precomputed:
        dist_X = X
    else:
        dist_X = pairwise_distances(X, squared=True)
    dist_X_embedded = pairwise_distances(X_embedded, squared=True)
    ind_X = np.argsort(dist_X, axis=1)
    ind_X_embedded = np.argsort(dist_X_embedded, axis=1)[:, 1:n_neighbors + 1]

    n_samples = X.shape[0]
    t = 0.0
    ranks = np.zeros(n_neighbors)
    for i in range(n_samples):
        for j in range(n_neighbors):
            ranks[j] = np.where(ind_X[i] == ind_X_embedded[i, j])[0][0]
        ranks -= n_neighbors
        t += np.sum(ranks[ranks > 0])
    t = 1.0 - t * (2.0 / (n_samples * n_neighbors *
                          (2.0 * n_samples - 3.0 * n_neighbors - 1.0)))
    return t