class_docstr: Computes the Poisson metric between labels and
  predictions.
  
  Usage:
  
  ```python
  pred = torch.tensor([[1, 1], [0, 0]])
  target = torch.tensor([[0, 1], [0, 0]])
  poisson = Poisson()
  poisson(pred, target)
  assert abs(poisson.compute() - 0.49999997) < 1e-6
  ```
class_docstr_tok: ["Computes","the","Poisson","metric","between","labels","and","predictions",".","Usage",":","``","`","python","pred","=","torch.tensor","(","[","[","1",",","1","]",",","[","0",",","0","]","]",")","target","=","torch.tensor","(","[","[","0",",","1","]",",","[","0",",","0","]","]",")","poisson","=","Poisson","(",")","poisson","(","pred",",","target",")","assert","abs","(","poisson.compute","(",")","-","0.49999997",")","<","1e-6","``","`"]
code: def compute(self):
      return self.poisson.float() / self.total
  
imports: import torch
  from abc import ABC, abstractmethod
label_desc: Use image node_id 3 for calling the Poisson obj's underlying member method code with example usage: obj.compute() and returns: unknown