code: def _sample_action(self, loc: torch.Tensor, scale_log: torch.Tensor):
      r = torch.randn_like(scale_log, device=scale_log.device)
      action = torch.tanh(loc + r * scale_log.exp())
      # Since each dim are independent, log-prob is simply sum
      log_prob = self.actor_network._log_prob(r, scale_log)
      squash_correction = self.actor_network._squash_correction(action)
      log_prob = torch.sum(log_prob - squash_correction, dim=1)
      return action, log_prob
  
file_path: ReAgent/reagent/gym/policies/samplers/continuous_sampler.py
imports: import reagent.core.types
  import torch
  from reagent.gym.types import GaussianSamplerScore, Sampler
label_desc: Use image node_id 2 for calling the GaussianSampler obj's underlying member method code with example usage: obj._sample_action(loc, scale_log) and returns: action,  log_prob