class_docstr: Coding `input - round(mean)` using `mean - round(mean)`
docstr: Evaluates a softplus function transitioning from 1/(1-x) to 1+x at x=0.
docstr_tok: ["Evaluates","a","softplus","function","transitioning","from","1\/","(","1-x",")","to","1+x","at","x=0","."]
code: def verysoftplus(inputs):
      """Evaluates a softplus function transitioning from 1/(1-x) to 1+x at x=0."""
      inputs_pos = torch.maximum(inputs, torch.zeros_like(inputs))
      inputs_neg = torch.minimum(inputs, torch.zeros_like(inputs))
      return torch.where(
          inputs > 0, inputs_pos + 1.0, 1.0 / (1.0 - inputs_neg)
      )
  
imports: import math
  from typing import Any, List, Optional, Tuple
  import scipy.stats
  import torch
  import torch.nn
  from compressai.entropy_models import EntropyModel
  from compressai.ops import LowerBound
  from torch import Tensor
label_desc: Use image node_id 3 for calling the GsnConditionalLocScaleShift obj's underlying member method code with example usage: obj.verysoftplus(inputs) and returns: torch