code: def inv_softplus(y: np.ndarray) -> np.ndarray:
      # y = log(1 + exp(x))  ==>  x = log(exp(y) - 1)
      return np.log(np.exp(y) - 1)
  
imports: from typing import List
  import numpy
  import pytest
  import torch
  import torch.nn
  from scipy.special import softmax
  from torch.distributions import Beta, Gamma, NegativeBinomial, Normal, Poisson, StudentT
  from torch.nn.utils import clip_grad_norm_
  from torch.optim import SGD
  from torch.utils.data import DataLoader, TensorDataset
  from gluonts.pydantic import PositiveFloat, PositiveInt
  from gluonts.torch.distributions import BetaOutput, DistributionOutput, GammaOutput, NegativeBinomialOutput, NormalOutput, PoissonOutput, SplicedBinnedPareto, SplicedBinnedParetoOutput, StudentTOutput
label_desc: Use image node_id 1 for calling a global function with example usage: inv_softplus(y) and returns: np