input: Write a python function to predict the outcome using logistic regression.
  assert LogisticRegression(np.array([[0, 1], [1, 1], [1, 0], [0, 0]]), np.array([0, 1, 1, 0])).predict(np.array([[0, 1], [1, 1]])) == [False, True]
  assert LogisticRegression(np.array([[2, 3], [3, 4], [4, 5], [5, 6]]), np.array([0, 1, 1, 0])).predict(np.array([[2, 3], [4, 5]])) == [False, True]
  assert LogisticRegression(np.array([[1, 2], [2, 3], [3, 4], [4, 5]]), np.array([0, 1, 0, 1])).predict(np.array([[1, 2], [3, 4]])) == [False, False]
output: from sklearn.linear_model import LogisticRegression as SklearnLogisticRegression
  import numpy as np
  
  class LogisticRegression:
      def __init__(self, X, y):
          self.model = SklearnLogisticRegression()
          self.model.fit(X, y)
      
      def predict(self, X):
          return self.model.predict(X).astype(bool).tolist()