class_docstr: Natural logarithm transformation.
  
  The Natural logarithm transformation can be used to make the data more normally
  distributed and stabilize its variance.
  
  Transforms each data point x to log(scale *(x+offset))
  
  Parameters
  ----------
  offset : float , default = 0
           Additive constant applied to all the data.
  scale  : float , default = 1
           Multiplicative scaling constant applied to all the data.
  
  See Also
  --------
  BoxCoxTransformer :
      Applies Box-Cox power transformation. Can help normalize data and
      compress variance of the series.
  sktime.transformations.series.exponent.ExponentTransformer :
      Transform input data by raising it to an exponent. Can help compress
      variance of series if a fractional exponent is supplied.
  sktime.transformations.series.exponent.SqrtTransformer :
      Transform input data by taking its square root. Can help compress
      variance of input series.
  
  Notes
  -----
  The log transformation is applied as :math:`ln(y)`.
  
  Examples
  --------
  >>> from sktime.transformations.series.boxcox import LogTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> transformer = LogTransformer()
  >>> y_hat = transformer.fit_transform(y)
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docstr: Transform X and return a transformed version.
  
  private _transform containing the core logic, called from transform
  
  Parameters
  ----------
  X : 2D np.ndarray
      Data to be transformed
  y : ignored argument for interface compatibility
      Additional data, e.g., labels for transformation
  
  Returns
  -------
  Xt : 2D np.ndarray
      transformed version of X
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code: def _transform(self, X, y=None):
      """Transform X and return a transformed version.
  
      private _transform containing the core logic, called from transform
  
      Parameters
      ----------
      X : 2D np.ndarray
          Data to be transformed
      y : ignored argument for interface compatibility
          Additional data, e.g., labels for transformation
  
      Returns
      -------
      Xt : 2D np.ndarray
          transformed version of X
      """
      offset = self.offset
      scale = self.scale
      Xt = np.log(scale * (X + offset))
      return Xt
  
imports: import numpy
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import is_int
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 2 for calling the LogTransformer obj's underlying member method code with example usage: obj._transform(X, y) and returns: Xt