class_docstr: Gaussian Conditional prior with mean and scale indexes
docstr: Convert prior parameters to CDF table indexes
  
  Args:
      params: tuple of tensors (means, scales)
  
  Returns:
      tensor of ints containing the actual CDF table indexes
docstr_tok: ["Convert","prior","parameters","to","CDF","table","indexes","Args",":","params",":","tuple","of","tensors","(","means",",","scales",")","Returns",":","tensor","of","ints","containing","the","actual","CDF","table","indexes"]
code: def build_indexes(self, params: Tuple[Tensor, Tensor]) -> Tensor:
      """
      Convert prior parameters to CDF table indexes
  
      Args:
          params: tuple of tensors (means, scales)
  
      Returns:
          tensor of ints containing the actual CDF table indexes
      """
      means, scales = params
      mean_grid = self._prior_mean[:, 0]  # all means equal along cols
      scale_grid = self._prior_scale[0]  # all scales equal along rows
  
      indexes_mean = self._find_idx_of_first_smallest(
          inputs=means, grid=mean_grid
      )
      indexes_scale = self._find_idx_of_first_smallest(
          inputs=scales, grid=scale_grid
      )
      return self.indexes_to_cdf_indexes(indexes_mean, indexes_scale)
  
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 13 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj.build_indexes(params) and returns: self