code: def soft_round_inverse(y, alpha, eps=1e-3):
      """Inverse of `soft_round`.
  
      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.
        eps: Float. Threshold below which `soft_round` is assumed to equal the
          identity function.
  
      Returns:
        `tf.Tensor`
      """
      # This guards the gradient of tf.where below against NaNs, while maintaining
      # correctness, as for alpha < eps the result is ignored.
      alpha_bounded = tf.maximum(alpha, eps)
      m = tf.floor(y) + 0.5
      s = (y - m) * (tf.tanh(alpha_bounded / 2.0) * 2.0)
      r = tf.atanh(s) / alpha_bounded
      # `r` must be between -.5 and .5 by definition. In case atanh becomes +-inf
      # due to numerical instability, this prevents the forward pass from yielding
      # infinite values. Note that it doesn't prevent the backward pass from
      # returning non-finite values.
      r = tf.clip_by_value(r, -0.5, 0.5)
  
      # For very low alphas, soft_round behaves like identity.
      return tf.where(alpha < eps, y, m + r, name="soft_round_inverse")
  
imports: import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: soft_round_inverse(y, alpha, eps) and returns: tf