class_docstr: Gaussian Conditional prior with mean and scale indexes
docstr: Forward pass: add uniform noise to inputs, compute their corresponding likelihood
  
  Args:
      inputs: tensor to be noised/quantized
      indexes: indexes of means and scales
      training: whether we are training. Defaults to None.
  
  Returns:
      tuple of tensors: noised input, its likelihood
docstr_tok: ["Forward","pass",":","add","uniform","noise","to","inputs",",","compute","their","corresponding","likelihood","Args",":","inputs",":","tensor","to","be","noised\/quantized","indexes",":","indexes","of","means","and","scales","training",":","whether","we","are","training",".","Defaults","to","None",".","Returns",":","tuple","of","tensors",":","noised","input",",","its","likelihood"]
code: def forward(
      self,
      inputs: Tensor,
      indexes: Tensor,
      training: Optional[bool] = None,
  ) -> Tuple[Tensor, Tensor]:
      """
      Forward pass: add uniform noise to inputs, compute their corresponding likelihood
  
      Args:
          inputs: tensor to be noised/quantized
          indexes: indexes of means and scales
          training: whether we are training. Defaults to None.
  
      Returns:
          tuple of tensors: noised input, its likelihood
      """
      training = self.training if training is None else training
      indexes = self._normalize_indexes(indexes)
      outputs = self.quantize(
          inputs, "noise" if training else "dequantize"
      )
      likelihood = self._likelihood(outputs, indexes)
  
      if self.use_likelihood_bound:
          likelihood = self.likelihood_lower_bound(likelihood)
      return outputs, 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 10 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj.forward(inputs, indexes, training) and returns: outputs,  likelihood