I have a torch tensor with shape (batch_size, number_maps, x_val, y_val). The tensor is normalized with a sigmoid function, so within range [0, 1] . I want to find the covariance for each map, so I want to have a tensor with shape (batch_size, number_maps, 2, 2). As far as I know, there is no torch.cov()  function as in numpy. How can I efficiently calculate the covariance without converting it to numpy?
I tried the following, but I’m pretty sure it’s not correct:
def get_covariance(tensor):
    bn, nk, w, h = tensor.shape
    tensor_reshape = tensor.reshape(bn, nk, 2, -1)
    x = tensor_reshape[:, :, 0, :]
    y = tensor_reshape[:, :, 1, :]
    mean_x = torch.mean(x, dim=2).unsqueeze(-1)
    mean_y = torch.mean(y, dim=2).unsqueeze(-1)
    
    xx = torch.sum((x - mean_x) * (x - mean_x), dim=2).unsqueeze(-1) / (h*w - 1)
    xy = torch.sum((x - mean_x) * (y - mean_y), dim=2).unsqueeze(-1) / (h*w - 1)
    yx = xy
    yy = torch.sum((y - mean_y) * (y - mean_y), dim=2).unsqueeze(-1) / (h*w - 1)
    
    cov = torch.cat((xx, xy, yx, yy), dim=2)
    cov = cov.reshape(bn, nk, 2, 2)

    return cov