code: def soft_round_inverse(
      y: torch.Tensor, alpha: float, eps: float = 1e-3
  ) -> torch.Tensor:
      """Inverse of ``soft_round``.
  
      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:
          y:
          alpha: smoothness of the approximation
          eps: threshold below which ``soft_round`` returns the identity
  
      Returns:
          The inverse of ``soft_round``.
      """
      maximum = torch.tensor(max(alpha, eps))
  
      m = torch.floor(y) + 0.5
  
      s = (y - m) * (torch.tanh(maximum / 2.0) * 2.0)
  
      r = torch.atanh(s) / maximum
  
      r = torch.clamp(r, -0.5, 0.5)
  
      x = m + r
  
      return torch.where(torch.tensor(alpha < eps), y, x)
  
label_desc: Use image node_id 1 for calling a global function with example usage: soft_round_inverse(y, alpha, eps) and returns: torch