[Python] How to apply the torch.inverse() function of PyTorch to every sample in the batch?

This may seem like a basic question, but I am unable to work it through.

In the forward pass of my neural network, I have an output tensor of shape 8x3x3, where 8 is my batch size. We can assume each 3x3 tensor to be a non-singular matrix. I need to find the inverse of these matrices. The PyTorch inverse() function only works on square matrices. Since I now have 8x3x3, how do I apply this function to every matrix in the batch in a differentiable manner?

If I iterate through the samples and append the inverses to a python list, which I then convert to a PyTorch tensor, should it be a problem during backprop? (I am asking since converting PyTorch tensors to numpy to perform some operations and then back to a tensor won't compute gradients during backprop for such operations)

I also get the following error when I try to do something like that.

a = torch.arange(0,8).view(-1,2,2)
b = [m.inverse() for m in a]
c = torch.FloatTensor(b)

TypeError: 'torch.FloatTensor' object does not support indexing
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