[Usage Question] Labels for "top k" best cluster assignments

Hello,

I am using kshape for time series data, and here are some general algorithm-agnostic clustering questions I have:

1. I would like to obtain the top-m best assignments, not just the top-1 as found in labels_. So labels_ would be of size (N x m) instead of (N,), with m <= K.

2. Inversely, I would like to obtain the top-m best samples for each cluster, i.e. the m samples most similar to a cluster's centroid. This would be a array of size (K x m), with m<=N.

3. To somewhat resume from point #1 and #2, I would like to obtain distance matrix from all samples to all clusters (N x K). This matrix by itself should allow me to compute for the quantities desired in #1 and #2. I see there is a dist matrix used in the source code. Is there an easy way to access it through the API without hacking the source code?

The issue points to tslearn/clustering/kshape.py around line 155, where a distance matrix is used internally. Start by reviewing that computation and the KShape API, then clarify which of the top-m assignments, top-m samples, or full N×K distance matrix should be supported. Done would require an agreed public API and corresponding validation.