When/Where do we require non-learnable parameters in deep learning?

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While going through the PyTorch documentation, I came across the require_grad
argument in tensors being set to True/False, wherein the author talks about scenarios where we wouldn't want the network to learn parameters. As per my knowledge, only Pooling has no parameters update, but that's because it has no parameters at all.

What are the scenarios when we have non-learnable parameters?

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