docstr: ivy.Array instance method variant of ivy.log_poisson_loss. This
  method simply wraps the function, and so the docstring for ivy.l1_loss
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      input array containing true labels.
  target
      input array containing targeted 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'``.
  out
      optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      The binary log-likelihood loss between the given distributions.
  
  
  Examples
  --------
  >>> x = ivy.array([0, 0, 1, 0])
  >>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
  >>> loss = x.log_poisson_loss(y)
  >>> print(loss)
  ivy.array([1.28402555, 1.28402555, 1.03402555, 1.28402555])
  
  >>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
  >>> loss = x.x.log_poisson_loss(z, reduction='mean')
  >>> print(loss)
  ivy.array(1.1573164)
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code: def log_poisson_loss(
      self: Union[ivy.Array, ivy.NativeArray],
      target: Union[ivy.Array, ivy.NativeArray],
      /,
      *,
      compute_full_loss: bool = False,
      axis: int = -1,
      reduction: str = "none",
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.log_poisson_loss. This
      method simply wraps the function, and so the docstring for ivy.l1_loss
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          input array containing true labels.
      target
          input array containing targeted 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'``.
      out
          optional output array, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          The binary log-likelihood loss between the given distributions.
  
  
      Examples
      --------
      >>> x = ivy.array([0, 0, 1, 0])
      >>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
      >>> loss = x.log_poisson_loss(y)
      >>> print(loss)
      ivy.array([1.28402555, 1.28402555, 1.03402555, 1.28402555])
  
      >>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
      >>> loss = x.x.log_poisson_loss(z, reduction='mean')
      >>> print(loss)
      ivy.array(1.1573164)
      """
      return ivy.log_poisson_loss(
          self._data,
          target,
          compute_full_loss=compute_full_loss,
          axis=axis,
          reduction=reduction,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/experimental/losses.py
imports: import abc
  from typing import Optional, Union
  import ivy
label_desc: Use image node_id 2 for calling the _ArrayWithLossesExperimental obj's underlying member method code with example usage: obj.log_poisson_loss() and returns: ivy