@Flock1 the issue was related to how you were accessing the parameter.  Either do dict(netM.module.model_enc1.named_parameters())['1.weight'] or do netM.module.model_enc1[1].weight.
@ani0075’s solution is correct (and related to the second option in the previous line of this answer) for when you want to refer to that module/parameter combination for the sake of pruning.
Beyond that, what do you mean by pruning “in one go”? Global pruning will allow you to prune the network by pooling all parameters together and comparing them with each other while deciding which ones to prune. That’s not the same thing as pruning each layer individually, but in an efficient way, without mixing weights across layers. Which one of the two were you interested in?