class_docstr: Coding `input - round(mean)` using `mean - round(mean)`
docstr: Get indexes from means and scales
docstr_tok: ["Get","indexes","from","means","and","scales"]
code: def _get_indexes(
      self, means: Tensor, scales: Tensor, training: bool = True
  ) -> Tensor:
      """
      Get indexes from means and scales
      """
      scales_i = self._scaler.to_scale_idx(
          self.verysoftplus(scales), training=training
      )
      mean_i = (means - self._round_st(means) + 0.5) * self._num_means
      return torch.stack([mean_i, scales_i], dim=-1)
  
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 4 for calling the GsnConditionalLocScaleShift obj's underlying member method code with example usage: obj._get_indexes(means, scales, training) and returns: torch