def onAutoIndentTriggered(self):
        """Indent current line or selected lines
        """
        cursor = self._qpart.textCursor()

        startBlock = self._qpart.document().findBlock(cursor.selectionStart())
        endBlock = self._qpart.document().findBlock(cursor.selectionEnd())

        if startBlock != endBlock:  # indent multiply lines
            stopBlock = endBlock.next()

            block = startBlock

            with self._qpart:
                while block != stopBlock:
                    self.autoIndentBlock(block, '')
                    block = block.next()
        else:  # indent 1 line
            self.autoIndentBlock(startBlock, '')
def gradient(self, idx, inputs):
        self.model.zero_grad()
        X = [x.requires_grad_() for x in inputs]
        outputs = self.model(*X)
        selected = [val for val in outputs[:, idx]]
        grads = []
        if self.interim:
            interim_inputs = self.layer.target_input
            for idx, input in enumerate(interim_inputs):
                grad = torch.autograd.grad(selected, input,
                                           retain_graph=True if idx + 1 < len(interim_inputs) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            del self.layer.target_input
            return grads, [i.detach().cpu().numpy() for i in interim_inputs]
        else:
            for idx, x in enumerate(X):
                grad = torch.autograd.grad(selected, x,
                                           retain_graph=True if idx + 1 < len(X) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            return grads