class_docstr: Gaussian Conditional prior with mean and scale indexes
docstr: Compute the likelihood of the convolved (Normal + Uniform noise)
docstr_tok: ["Compute","the","likelihood","of","the","convolved","(","Normal","+","Uniform","noise",")"]
code: def _likelihood(self, inputs: Tensor, indexes: Tensor) -> Tensor:
      """
      Compute the likelihood of the convolved (Normal + Uniform noise)
      """
      half = float(0.5)
      means, scales = self._get_prior_params(
          means_idx=indexes[..., 0], scales_idx=indexes[..., 1]
      )
      values = torch.abs(inputs - means)
  
      upper = self._standardized_cumulative((half - values) / scales)
      lower = self._standardized_cumulative((-half - values) / scales)
      likelihood = upper - lower
  
      return likelihood
  
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 8 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj._likelihood(inputs, indexes) and returns: likelihood