def batchwise_ranking_regularizer(features, targets, interp_strength_lambda):
    loss = 0

    # Reduce ties and boost relative representation of infrequent labels by computing the 
    # regularizer over a subset of the batch in which each label appears at most once
    batch_unique_targets = torch.unique(targets)
    if len(batch_unique_targets) < len(targets):
        sampled_indices = []
        for target in batch_unique_targets:
            sampled_indices.append(random.choice((targets == target).nonzero()[:,0]).item())
        x = features[sampled_indices]
        y = targets[sampled_indices]
    else:
        x = features
        y = targets

    # Compute feature similarities for ranking
    xxt = torch.matmul(F.normalize(x.view(x.size(0),-1)), F.normalize(x.view(x.size(0),-1)).permute(1,0))

    # Compute the loss of ranking similarity
    for i in range(len(y)):

        label_ranks = rank_normalised(-torch.abs(y[i] - y).transpose(0,1))
        feature_ranks = TrueRanker.apply(xxt[i].unsqueeze(dim=0), interp_strength_lambda) # differentiable ranking operation, defined in ranking.py
        loss += F.mse_loss(feature_ranks, label_ranks)

    return loss