class_docstr: Apply a log transformation to an endogenous array
  
  When ``y`` is your endogenous array, the log transform is
  ``log(y + lmbda)``
  
  Parameters
  ----------
  
  lmbda : float, optional (default=0.)
      The value to add to ``y`` to make it non-negative. If, after adding
      ``lmbda``, there are still negative values, a ValueError will be
      raised.
  
  neg_action : str, optional (default="raise")
      How to respond if any values in ``y <= 0`` after adding ``lmbda``.
      One of ('raise', 'warn', 'ignore'). If anything other than 'raise',
      values <= 0 will be truncated to the value of ``floor``.
  
  floor : float, optional (default=1e-16)
      A positive value that truncate values to if there are values in ``y``
      that are zero or negative and ``neg_action`` is not 'raise'. Note that
      if values are truncated, invertibility will not be preserved, and the
      transformed array may not be perfectly inverse-transformed.
docstr: Inverse transform a transformed array
  
  Inverse the log transformation on the transformed array. Note that
  if truncation happened in the ``transform`` method, invertibility will
  not be preserved, and the transformed array may not be perfectly
  inverse-transformed.
  
  Parameters
  ----------
  y : array-like or None, shape=(n_samples,)
      The transformed endogenous (time-series) array.
  
  X : array-like or None, shape=(n_samples, n_features), optional
      The exogenous array of additional covariates. Not used for
      endogenous transformers. Default is None, and non-None values will
      serve as pass-through arrays.
  
  Returns
  -------
  y : array-like or None
      The inverse-transformed y array
  
  X : array-like or None
      The inverse-transformed exogenous array
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code: def inverse_transform(
      self, y, X=None, **kwargs
  ):  # TODO: kwargs go away
      """Inverse transform a transformed array
  
      Inverse the log transformation on the transformed array. Note that
      if truncation happened in the ``transform`` method, invertibility will
      not be preserved, and the transformed array may not be perfectly
      inverse-transformed.
  
      Parameters
      ----------
      y : array-like or None, shape=(n_samples,)
          The transformed endogenous (time-series) array.
  
      X : array-like or None, shape=(n_samples, n_features), optional
          The exogenous array of additional covariates. Not used for
          endogenous transformers. Default is None, and non-None values will
          serve as pass-through arrays.
  
      Returns
      -------
      y : array-like or None
          The inverse-transformed y array
  
      X : array-like or None
          The inverse-transformed exogenous array
      """
      return super().inverse_transform(y, X, **kwargs)
  
imports: from .boxcox import BoxCoxEndogTransformer
label_desc: Use image node_id 4 for calling the LogEndogTransformer obj's underlying member method code with example usage: obj.inverse_transform(y, X) and returns: super