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