input: Write a python function to detect outliers in a dataset using the EllipticEnvelope method from sklearn. 
  assert np.array_equal(outlier_detection(np.array([[1], [2], [3], [4], [100]])), np.array([[100]]))
  assert np.array_equal(outlier_detection(np.array([[10], [12], [14], [16], [1000]])), np.array([[1000]]))
  assert np.array_equal(outlier_detection(np.array([[1], [2], [1.5], [2.5], [2.1], [100]])), np.array([[100]]))
output: from sklearn.covariance import EllipticEnvelope
  import numpy as np
  
  def outlier_detection(data):
      envelope = EllipticEnvelope(contamination=0.1)
      envelope.fit(data)
      predictions = envelope.predict(data)
      outliers = data[predictions == -1]
      return outliers