code: def logprob(self, action_sample, pd_params):
          
          means = pd_params[:, :, 0]
          log_std = pd_params[:, :, 1]
  
          std = torch.exp(log_std)
  
          z_score = (action_sample - means) / std
  
          return - (0.5 * ((z_score**2 + self.LOG2PI).sum(dim=-1)) + log_std.sum(dim=-1))
docs: Log-likelihood