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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def comb_similarity(t1: torch.Tensor, t2: torch.Tensor, **kwargs):
        # https://github.com/pytorch/pytorch/issues/11202
        "Computes the similarity function between each embedding of `t1` and `t2` matrices."
        print('Computing similarities...')

        w1 = t1.norm(p=2, dim=1, keepdim=True)
        w2 = w1 if t2 is t1 else t2.norm(p=2, dim=1, keepdim=True)

        t = torch.mm(t1, t2.t()) / (w1 * w2.t()).clamp(min=1e-8)
        return torch.tril(t, diagonal=-1)