input: Write a Python function to predict gender using SVM and KNN models.
  ```python
  from sklearn import svm
  from sklearn.neighbors import KNeighborsClassifier
  
  def predict_gender(features):
      X = [[180, 25], [170, 27], [150, 40], [145, 37], [177, 23]]
      y = ['male', 'female', 'female', 'male', 'female']
      
      svm_model = svm.SVC(gamma='auto', decision_function_shape='ovo')
      knn_model = KNeighborsClassifier(n_neighbors=5)
      
      svm_model.fit(X, y)
      knn_model.fit(X, y)
      
      pred_svm = svm_model.predict([features])
      pred_knn = knn_model.predict([features])
      
      return pred_svm[0], pred_knn[0]
  
  assert predict_gender([160, 32]) == ('female', 'female')
  assert predict_gender([140, 22]) == ('female', 'female')
  assert predict_gender([190, 29]) == ('male', 'male')
output: def predict_gender(features):
      X = [[180, 25], [170, 27], [150, 40], [145, 37], [177, 23]]
      y = ['male', 'female', 'female', 'male', 'female']
      
      svm_model = svm.SVC(gamma='auto', decision_function_shape='ovo')
      knn_model = KNeighborsClassifier(n_neighbors=5)
      
      svm_model.fit(X, y)
      knn_model.fit(X, y)
      
      pred_svm = svm_model.predict([features])
      pred_knn = knn_model.predict([features])
      
      return pred_svm[0], pred_knn[0]