input: Write a python function to detect anomalies in a dataset using DBSCAN clustering.
  assert detect_anomalies(np.array([[1, 2], [2, 2], [3, 2], [8, 7], [8, 8], [25, 80]])) == [False, False, False, False, False, True]
  assert detect_anomalies(np.array([[1, 1], [2, 2], [2, 3], [8, 8], [9, 9], [10, 10]])) == [False, False, False, False, False, False]
  assert detect_anomalies(np.array([[0, 0], [0, 1], [1, 0], [1, 1], [10, 10]])) == [False, False, False, False, True]
output: from sklearn.cluster import DBSCAN
  import numpy as np
  
  def detect_anomalies(data):
      db = DBSCAN(eps=3, min_samples=2).fit(data)
      labels = db.labels_
      return [label == -1 for label in labels]