def METHOD_NAME(self, dataset_api=None):
    """
    Return all neighbours of the architecture
    """
    neighbours = []
    for idx in range(len(self.channels)):
        candidates = self.channel_candidates.copy()
        candidates.remove(self.channels[idx])
        for channels in candidates:
            neighbour_channels = list(self.channels).copy()
            neighbour_channels[idx] = channels
            neighbour = NATSBenchSizeSearchSpace()
            neighbour.set_channels(neighbour_channels)
            neighbour_model = torch.nn.Module()
            neighbour_model.arch = neighbour
            neighbours.append(neighbour_model)
    random.shuffle(neighbours)
    return neighbours