docstr: Perform forward pass of the Log Poisson Loss.
  
  true
      input array containing true labels.
  pred
      input array containing Predicted labels.
  compute_full_loss
      whether to compute the full loss. If false, a constant term is dropped
      in favor of more efficient optimization. Default: ``False``.
  axis
      the axis along which to compute the log-likelihood loss. If axis is ``-1``,
      the log-likelihood loss will be computed along the last dimension.
      Default: ``-1``.
  reduction
      ``'none'``: No reduction will be applied to the output.
      ``'mean'``: The output will be averaged.
      ``'sum'``: The output will be summed. Default: ``'none'``.
  
  Returns
  -------
  ret
      The binary log-likelihood loss between the given distributions.
docstr_tok: ["Perform","forward","pass","of","the","Log","Poisson","Loss",".","true","input","array","containing","true","labels",".","pred","input","array","containing","Predicted","labels",".","compute_full_loss","whether","to","compute","the","full","loss",".","If","false",",","a","constant","term","is","dropped","in","favor","of","more","efficient","optimization",".","Default",":","``","False","``",".","axis","the","axis","along","which","to","compute","the","log-likelihood","loss",".","If","axis","is","``","-1","``",",","the","log-likelihood","loss","will","be","computed","along","the","last","dimension",".","Default",":","``","-1","``",".","reduction","``","'none","'","``",":","No","reduction","will","be","applied","to","the","output",".","``","'mean","'","``",":","The","output","will","be","averaged",".","``","'sum","'","``",":","The","output","will","be","summed",".","Default",":","``","'none","'","``",".","Returns","--","--","--","-","ret","The","binary","log-likelihood","loss","between","the","given","distributions","."]
code: def _forward(
      self,
      true,
      pred,
      *,
      compute_full_loss=None,
      axis=None,
      reduction=None,
  ):
      """Perform forward pass of the Log Poisson Loss.
  
      true
          input array containing true labels.
      pred
          input array containing Predicted labels.
      compute_full_loss
          whether to compute the full loss. If false, a constant term is dropped
          in favor of more efficient optimization. Default: ``False``.
      axis
          the axis along which to compute the log-likelihood loss. If axis is ``-1``,
          the log-likelihood loss will be computed along the last dimension.
          Default: ``-1``.
      reduction
          ``'none'``: No reduction will be applied to the output.
          ``'mean'``: The output will be averaged.
          ``'sum'``: The output will be summed. Default: ``'none'``.
  
      Returns
      -------
      ret
          The binary log-likelihood loss between the given distributions.
      """
      return ivy.log_poisson_loss(
          true,
          pred,
          compute_full_loss=ivy.default(
              compute_full_loss, self._compute_full_loss
          ),
          axis=ivy.default(axis, self._axis),
          reduction=ivy.default(reduction, self._reduction),
      )
  
file_path: ivy/ivy/stateful/losses.py
imports: import ivy
  from ivy.stateful.module import Module
label_desc: Use image node_id 2 for calling the LogPoissonLoss obj's underlying member method code with example usage: obj._forward(true, pred) and returns: ivy