Confusion in time steps mentioned in the paper!

[HOMGH]

Hi,
Based on the paper, the later time steps, the more informative feature representations. But how can we see the effect of different time steps using the code? As far as I can see in the feature_extractors.py, in

ddpm-segmentation/src/feature_extractors.py

Line 72 in 9a13776

class FeatureExtractorDDPM(FeatureExtractor):

with different values of "t", we would have different noisy images (with different level of noise, based on the t. The larger "t" corresponds to more noise in the image). So how can this "t" work as the time steps in reverse process?
Thanks.