class_docstr: Class defining poisson distribution type functionality
docstr: Exponential inverse link function for poisson data.
  
  Args:
      X_hat: Reconstructed data in the latent space.
  
  Returns:
      Modified X_hat.
      MUST be same shape as passed in data.
      Projects the self.idx column from the latent space to the dist_type space.
docstr_tok: ["Exponential","inverse","link","function","for","poisson","data",".","Args",":","X_hat",":","Reconstructed","data","in","the","latent","space",".","Returns",":","Modified","X_hat",".","MUST","be","same","shape","as","passed","in","data",".","Projects","the","self.idx","column","from","the","latent","space","to","the","dist_type","space","."]
code: def inverse_link_function(self, X_hat: torch.Tensor) -> torch.Tensor:
      """
      Exponential inverse link function for poisson data.
  
      Args:
          X_hat: Reconstructed data in the latent space.
  
      Returns:
          Modified X_hat.
          MUST be same shape as passed in data.
          Projects the self.idx column from the latent space to the dist_type space.
      """
      X_hat[:, self.idx] = torch.exp(X_hat[:, self.idx])
      return X_hat
  
file_path: causalnex/causalnex/structure/pytorch/dist_type/poisson.py
imports: import numpy
  import torch
  from torch import nn
  from causalnex.structure.pytorch.dist_type._base import DistTypeBase
label_desc: Use image node_id 3 for calling the DistTypePoisson obj's underlying member method code with example usage: obj.inverse_link_function(X_hat) and returns: X_hat