input: Write a python function to calculate the cross-entropy loss between two probability distributions.
  assert CrossEntropy().loss(np.array([1, 0, 0]), np.array([0.8, 0.1, 0.1])) == -np.log(0.8)
  assert CrossEntropy().loss(np.array([0, 1, 0]), np.array([0.2, 0.5, 0.3])) == -np.log(0.5)
  assert CrossEntropy().loss(np.array([0.5, 0.5]), np.array([0.7, 0.3])) == -0.5 * np.log(0.7) - 0.5 * np.log(0.3)
output: import numpy as np
  
  class CrossEntropy:
      def loss(self, y_true, y_pred):
          return -np.sum(y_true * np.log(y_pred))