class_docstr: Gaussian Conditional prior with mean and scale indexes
docstr: Ensure indexes are within the allowed bounds in a differentiable way
docstr_tok: ["Ensure","indexes","are","within","the","allowed","bounds","in","a","differentiable","way"]
code: def _normalize_indexes(self, indexes: Tensor) -> Tensor:
      """
      Ensure indexes are within the allowed bounds in a differentiable way
      """
      indexes = self.lower_bound_zero(indexes)
      # NOTE: these are actually LowerBounds, so use -f(-input), see `__init__`
      i0 = -self.upper_bound_mean(-indexes[..., 0])
      i1 = -self.upper_bound_scale(-indexes[..., 1])
      return torch.stack([i0, i1], 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 9 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj._normalize_indexes(indexes) and returns: torch