def train(self, X, y):
    '\n        Train the classifier. For k-nearest neighbors this is just\n        memorizing the training data.\n\n        Inputs:\n        - X: A numpy array of shape (num_train, D) containing the training data\n          consisting of num_train samples each of dimension D.\n        - y: A numpy array of shape (N,) containing the training labels, where\n             y[i] is the label for X[i].\n        '
    self.X_train = X
    self.y_train = y
Train the classifier. For k-nearest neighbors this is just
memorizing the training data.

Inputs:
- X: A numpy array of shape (num_train, D) containing the training data
  consisting of num_train samples each of dimension D.
- y: A numpy array of shape (N,) containing the training labels, where
     y[i] is the label for X[i].
def train(self, X, y):
    '\n        Train the classifier. For k-nearest neighbors this is just\n        memorizing the training data.\n\n        Inputs:\n        - X: A numpy array of shape (num_train, D) containing the training data\n          consisting of num_train samples each of dimension D.\n        - y: A numpy array of shape (N,) containing the training labels, where\n             y[i] is the label for X[i].\n        '
    self.X_train = X
    self.y_train = y