input: Write a python function to evaluate the accuracy of a classifier using numpy.
  assert evaluate_accuracy(np.array([1, 0, 1, 1]), np.array([1, 0, 0, 1])) == 0.75
  assert evaluate_accuracy(np.array([0, 0, 1, 1]), np.array([0, 1, 0, 1])) == 0.5
  assert evaluate_accuracy(np.array([1, 1, 1, 0]), np.array([1, 1, 1, 0])) == 1.0
output: import numpy as np
  
  def evaluate_accuracy(y_true, y_pred):
      correct_predictions = np.sum(y_true == y_pred)
      total_predictions = len(y_true)
      accuracy = correct_predictions / total_predictions
      return accuracy