code: def soft_round(
      x: torch.Tensor, alpha: float, eps: float = 1e-3
  ) -> torch.Tensor:
      """Differentiable approximation of ``torch.round``.
  
      A larger ``alpha`` correspond to a closer approximation of ``round``. If
          ``alpha`` is close to zero, this function reduces to the identity.
  
      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 approximation
          eps: threshold below which ``soft_round`` returns the identity
  
      Returns:
          The differentiable approximation of ``torch.round``.
      """
      maximum = torch.tensor(max(alpha, eps))
  
      m = torch.floor(x) + 0.5
  
      z = torch.tanh(maximum / 2.0) * 2.0
  
      y = m + torch.tanh(maximum * (x - m)) / z
  
      return torch.where(torch.tensor(alpha < eps), x, y)
  
label_desc: Use image node_id 1 for calling a global function with example usage: soft_round(x, alpha, eps) and returns: torch