Per-sample weights or label fractions

Hi there.

Just wondering if per-sample weighting is currently possible? For our project we need to apply fractional weights to each sample.

Alternatively can DL4J handle having a fraction specified for label values for each sample? This example would indicate that it can: https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/quickstart/modeling/feedforward/regression/ImageDrawer.java

However, the Constructor for DataSet specifies that labels must be binarized: https://deeplearning4j.org/api/latest/org/nd4j/linalg/dataset/DataSet.html#DataSet-org.nd4j.linalg.api.ndarray.INDArray-org.nd4j.linalg.api.ndarray.INDArray-org.nd4j.linalg.api.ndarray.INDArray-org.nd4j.linalg.api.ndarray.INDArray-

I’m confused as this seems to be contradictory.

Any advice is greatly appreciated.