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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 update_location_centroid(point, cluster, max_distance, min_samples):
 """ Updates the centroid of a location cluster with another point Args: point (:obj:`Point`):
 Point to add to the cluster cluster (:obj:`list` of :obj:`Point`):
 Location cluster max_distance (float):
 Max neighbour distance min_samples (int):
 Minimum number of samples Returns: (:obj:`Point`, :obj:`list` of :obj:`Point`):
 Tuple with the location centroid and new point cluster (given cluster + given point) """

    cluster.append(point)
    points = [p.gen2arr() for p in cluster]

    # Estimates the epsilon
    eps = estimate_meters_to_deg(max_distance, precision=6)

    p_cluster = DBSCAN(eps=eps, min_samples=min_samples)
    p_cluster.fit(points)

    clusters = {}
    for i, label in enumerate(p_cluster.labels_):
        if label in clusters.keys():
            clusters[label].append(points[i])
        else:
            clusters[label] = [points[i]]

    centroids = []
    biggest_centroid_l = -float("inf")
    biggest_centroid = None

    for label, n_cluster in clusters.items():
        centroid = compute_centroid(n_cluster)
        centroids.append(centroid)

        if label >= 0 and len(n_cluster) >= biggest_centroid_l:
            biggest_centroid_l = len(n_cluster)
            biggest_centroid = centroid

    if biggest_centroid is None:
        biggest_centroid = compute_centroid(points)

    return biggest_centroid, cluster