class_docstr: Gaussian Conditional prior with mean and scale indexes
docstr: Map indexes to the parameters of the gaussian prior
  
  Args:
      means_idx: tensor of mean indexes
      scales_idx: tensor of scale indexes
  
  Returns:
      Tuple (means, scales)
docstr_tok: ["Map","indexes","to","the","parameters","of","the","gaussian","prior","Args",":","means_idx",":","tensor","of","mean","indexes","scales_idx",":","tensor","of","scale","indexes","Returns",":","Tuple","(","means",",","scales",")"]
code: def _get_prior_params(
      self, means_idx: Tensor, scales_idx: Tensor
  ) -> Tuple[Tensor, Tensor]:
      """
      Map indexes to the parameters of the gaussian prior
  
      Args:
          means_idx: tensor of mean indexes
          scales_idx: tensor of scale indexes
  
      Returns:
          Tuple (means, scales)
      """
      scales = self._scaler.from_scale_idx(scales_idx)
      means = means_idx / self._num_means - 0.5 * self._not_one_mean
      return means, scales
  
imports: import math
  from typing import Any, List, Optional, Tuple
  import scipy.stats
  import torch
  import torch.nn
  from compressai.entropy_models import EntropyModel
  from compressai.ops import LowerBound
  from torch import Tensor
label_desc: Use image node_id 2 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj._get_prior_params(means_idx, scales_idx) and returns: means,  scales