code: def soft_round_conditional_mean(
      x: torch.Tensor, alpha: float
  ) -> torch.Tensor:
      """Conditional mean of ``x`` given noisy soft rounded values.
  
      Computes:
  
      .. math::
  
      g(z) = E[Y | s(Y) + U = z]
  
          where :math:`s` is the soft-rounding function, :math:`U` is uniform
          between :math:`-0.5` and :math:`0.5` and :math:`Y` is considered
          uniform when truncated to the interval :math:`[z-0.5, z+0.5]`.
  
      This operation is described in Sec. 4.1. in the paper:
  
      > "Universally Quantized Neural Compression"<br />
      > Eirikur Agustsson & Lucas Theis<br />
      > https://arxiv.org/abs/2006.09952
  
      Args:
          x: The input tensor.
          alpha: smoothness of the ``soft_round`` approximation
  
      Returns:
          The conditional mean, of same shape as ``x``.
      """
      return soft_round_inverse(x - 0.5, alpha) + 0.5
  
label_desc: Use image node_id 1 for calling a global function with example usage: soft_round_conditional_mean(x, alpha) and returns: unknown