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: Get parameters for this estimator.
  
  Parameters
  ----------
  deep : bool, default=True
      If True, will return the parameters for this estimator and
      contained subobjects that are estimators.
  
  Returns
  -------
  params : mapping of string to any
      Parameter names mapped to their values.
docstr_tok: ["Get","parameters","for","this","estimator",".","Parameters","--","--","--","--","--","deep",":","bool",",","default=True","If","True",",","will","return","the","parameters","for","this","estimator","and","contained","subobjects","that","are","estimators",".","Returns","--","--","--","-","params",":","mapping","of","string","to","any","Parameter","names","mapped","to","their","values","."]
code: def get_params(self, deep=True):
      """Get parameters for this estimator.
  
      Parameters
      ----------
      deep : bool, default=True
          If True, will return the parameters for this estimator and
          contained subobjects that are estimators.
  
      Returns
      -------
      params : mapping of string to any
          Parameter names mapped to their values.
      """
      # this method is shadowed because we shadow the the lmbda parameter
      # name. Instead of setting lmbda, this transformer stores the parameter
      # as lmbda2
      # See: https://github.com/alkaline-ml/pmdarima/issues/407
      # Thanks to @jseabold
      params = super().get_params(deep=deep)
      params["lmbda"] = self.lmbda2
      return params
  
imports: from .boxcox import BoxCoxEndogTransformer
label_desc: Use image node_id 5 for calling the LogEndogTransformer obj's underlying member method code with example usage: obj.get_params(deep) and returns: params