完善以下代码：def squared_distance_matrix(xs: np.ndarray, ys: np.ndarray) -> np.ndarray: """ Construct a N x M distance matrix from a data matrix with size (N, D) Each element d_{ij} = ||x_i - y_j ||_2^2 :param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of points :return: a distance matrix with size (N, M) """ # TODO: change the code below and implement your distance computation method. return

Here is the code for computing the squared distance matrix between two sets of points:

def squared_distance_matrix(xs: np.ndarray, ys: np.ndarray) -> np.ndarray:
    """
    Construct a N x M distance matrix from a data matrix with size (N, D)
    Each element d_{ij} = ||x_i - y_j ||_2^2
    
    :param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points
    :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of points
    :return:
        a distance matrix with size (N, M)
    """
    N, D = xs.shape
    M, _ = ys.shape
    result = np.zeros((N, M))
    for i in range(N):
        for j in range(M):
            diff = xs[i] - ys[j]
            result[i, j] = np.dot(diff, diff)
    return result

The function takes two arrays `xs` and `ys` as input, which represent sets of points with sizes `(N, D)` and `(M, D)` respectively. The function returns a distance matrix with size `(N, M)`, where each element `d_{ij}` represents the squared Euclidean distance between point `xs[i]` and point `ys[j]`.

The function first gets the number of samples `N` and `M`, as well as the dimension `D` of the points. It then initializes the result matrix with zeros. The function then loops through each pair of points in `xs` and `ys`, and computes the squared Euclidean distance between them using the dot product of their difference vector with itself. The result is stored in the corresponding element of the result matrix.

Note that this implementation assumes that the input arrays `xs` and `ys` are numpy arrays.