Perhaps start with
scores = torch.cat(output_list,0)
probs = torch.softmax(scores, dim=-1)
log_probs = probs.clamp_min(1e-6).log()
per_class_entropy = probs*log_probs #shape: (num_models, batch, num_classes)

At this point, I’m not sure about “per-class entropy” formula, that would dictate how to reduce the last tensor. Perhaps -per_class_entropy.mean((0,1))