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
```