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 nonlinear_1d(module, grad_input, grad_output):
    delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]

    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # handles numerical instabilities where delta_in is very small by
    # just taking the gradient in those cases
    grads = [None for _ in grad_input]
    grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
                           grad_output[0] * (delta_out / delta_in).repeat(dup0))
    return tuple(grads)