code: def sample(self, params, argmax_sampling=False):
          
          means = params[:, :, 0]
          log_std = params[:, :, 1]
  
          if argmax_sampling:
              return means
          else:
              return torch.randn_like(means) * torch.exp(log_std) + means
docs: Sample from a probability space of all actions