Let's say you would like to generate just the first feature of 1000 samples with label 0.
The distribution of the first feature conditioned on label 1 follows a Bernoulli distribution (as suggested by the name) with parameter "exp(feature_log_prob_[0, 0])". You could then generate the first feature of these 1000 samples by just doing first_feature = bernoulli.rvs(exp(feature_log_prob_[0, 0]), size=1000) And follow the same approach for all the other features with the corresponding parameters. (They are conditionally independent)
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