class_docstr: Gaussian Conditional prior with mean and scale indexes
docstr: Convert indexes to CDF table indexes
  
  Args:
      indexes_mean: mean indexes (ints or floats of ints)
      indexes_scale: scale indexes (ints or floats of ints)
  
  Returns:
      tensor of ints containing the actual CDF table indexes
docstr_tok: ["Convert","indexes","to","CDF","table","indexes","Args",":","indexes_mean",":","mean","indexes","(","ints","or","floats","of","ints",")","indexes_scale",":","scale","indexes","(","ints","or","floats","of","ints",")","Returns",":","tensor","of","ints","containing","the","actual","CDF","table","indexes"]
code: def indexes_to_cdf_indexes(
      self, indexes_mean: Tensor, indexes_scale: Tensor
  ) -> Tensor:
      """
      Convert indexes to CDF table indexes
  
      Args:
          indexes_mean: mean indexes (ints or floats of ints)
          indexes_scale: scale indexes (ints or floats of ints)
  
      Returns:
          tensor of ints containing the actual CDF table indexes
      """
      # cdf is pmf_length = num_means*num_scales long, where means and scalea are
      # arranged as [mean_0]*num_means, all scales, [mean_1]*num_means, all scales ...
      # so the correct indexing is:
      cdf_indexes = (
          indexes_mean.int() * self._num_scales + indexes_scale.int()
      )
      return cdf_indexes
  
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 12 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj.indexes_to_cdf_indexes(indexes_mean, indexes_scale) and returns: cdf_indexes