class_docstr: Computes the Poisson regression loss.
  MSEMetric for this loss will be reporting the mean
  square error between lengths, not length ratios!
class_docstr_tok: ["Computes","the","Poisson","regression","loss",".","MSEMetric","for","this","loss","will","be","reporting","the","mean","square","error","between","lengths",",","not","length","ratios","!"]
code: def __init__(
      self,
      name: str = f"{C.LENRATIO_NAME}_{C.LINK_POISSON}",
      weight: float = 1.0,
      output_name: str = C.LENRATIO_NAME,
      label_name: str = C.LENRATIO_LABEL_NAME,
  ) -> None:
      super().__init__(
          name=name,
          output_name=output_name,
          label_name=label_name,
          weight=weight,
      )
  
imports: import logging
  import math
  from abc import ABC, abstractmethod
  from typing import Any, Dict, Optional, Tuple
  import torch
  import numpy
  from .None import constants
  from .None import utils
label_desc: Use image node_id 1 to create a new PoissonLoss object from inherited base classes: Loss with example: obj = PoissonLoss(name, weight, output_name, label_name)