def handle_rel_positions(self, x):
        """
        Handles the vector or relative positions which is given to KernelNet.
        """
        if self.rel_positions is None:
            if self.train_length[0] == 0:
                # The ckconv has not been trained yet. Set maximum length to be 1.
                self.train_length[0] = x.shape[-1]

            # Calculate the maximum relative position based on the length of the train set,
            # and the current length of the input.
            max_relative_pos = self.calculate_max(
                self.train_length.item(), current_length=x.shape[-1]
            )

            # Creates the vector of relative positions.
            self.rel_positions = (
                torch.linspace(-1.0, max_relative_pos, x.shape[-1])
                    .cuda()
                    .unsqueeze(0)
                    .unsqueeze(0)
            )  # -> With form: [batch_size=1, in_channels=1, x_dimension]

            # calculate and save the sr ratio for later
            if self.train_length.item() > x.shape[-1]:
                self.sr_change = round(self.train_length.item() / x.shape[-1])
            else:
                self.sr_change = 1 / round(x.shape[-1] / self.train_length.item())

            # if new signal has higher frequency
            if self.sr_change < 1:
                self.sigma = 0.5

        return self.rel_positions

        Handles the vector or relative positions which is given to KernelNet.
        
analyze/convnexts4nd/src/models/baselines/ckconv.py