code: def soft_round_conditional_mean(y, alpha):
      """Conditional mean of inputs given noisy soft rounded values.
  
      Computes g(z) = E[Y | s(Y) + U = z] where s is the soft-rounding function,
      U is uniform between -0.5 and 0.5 and Y is considered uniform when truncated
      to the interval [z-0.5, z+0.5].
  
      This 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:
        y: `tf.Tensor`. Inputs to this function.
        alpha: Float or `tf.Tensor`. Controls smoothness of the approximation.
  
      Returns:
        The conditional mean, of same shape as `inputs`.
      """
      return soft_round_inverse(y - 0.5, alpha) + 0.5
  
imports: import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: soft_round_conditional_mean(y, alpha) and returns: unknown