def METHOD_NAME(y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
    """Computes the FID score metric on a batch of feature vectors.
    Args:
        y_pred: feature vectors extracted from a pretrained network run on generated images.
        y: feature vectors extracted from a pretrained network run on images from the real data distribution.
    """
    y = y.double()
    y_pred = y_pred.double()
    if y.ndimension() > 2:
        raise ValueError("Inputs should have (number images, number of features) shape.")
    mu_y_pred = torch.mean(y_pred, dim=0)
    sigma_y_pred = _cov(y_pred, rowvar=False)
    mu_y = torch.mean(y, dim=0)
    sigma_y = _cov(y, rowvar=False)
    return compute_frechet_distance(mu_y_pred, sigma_y_pred, mu_y, sigma_y)