code: def _log_prob(
      self,
      loc: torch.Tensor,
      scale_log: torch.Tensor,
      squashed_action: torch.Tensor,
  ):
      # This is not getting exported; we can use it
      n = torch.distributions.Normal(loc, scale_log.exp())
      raw_action = self.actor_network._atanh(squashed_action)
      log_prob = n.log_prob(raw_action)
      squash_correction = self.actor_network._squash_correction(
          squashed_action
      )
      log_prob = torch.sum(log_prob - squash_correction, dim=1)
      return 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 4 for calling the GaussianSampler obj's underlying member method code with example usage: obj._log_prob(loc, scale_log, squashed_action) and returns: log_prob