class_docstr: Constructs a transformer from an arbitrary callable.
  
  A FunctionTransformer forwards its X (and optionally y) arguments to a
  user-defined function or function object and returns the result of this
  function. This is useful for stateless transformations such as taking the
  log of frequencies, doing custom scaling, etc.
  
  A FunctionTransformer will not do any checks on its function's output.
  
  Note: If a lambda is used as the function, then the resulting
  transformer will not be pickleable.
  
  .. versionadded:: 0.17
  
  Read more in the :ref:`User Guide <function_transformer>`.
  
  Parameters
  ----------
  func : callable, optional default=None
      The callable to use for the transformation. This will be passed
      the same arguments as transform, with args and kwargs forwarded.
      If func is None, then func will be the identity function.
  
  inverse_func : callable, optional default=None
      The callable to use for the inverse transformation. This will be
      passed the same arguments as inverse transform, with args and
      kwargs forwarded. If inverse_func is None, then inverse_func
      will be the identity function.
  
  validate : bool, optional default=True
      Indicate that the input X array should be checked before calling
      func. If validate is false, there will be no input validation.
      If it is true, then X will be converted to a 2-dimensional NumPy
      array or sparse matrix. If this conversion is not possible or X
      contains NaN or infinity, an exception is raised.
  
  accept_sparse : boolean, optional
      Indicate that func accepts a sparse matrix as input. If validate is
      False, this has no effect. Otherwise, if accept_sparse is false,
      sparse matrix inputs will cause an exception to be raised.
  
  pass_y : bool, optional default=False
      Indicate that transform should forward the y argument to the
      inner callable.
  
  kw_args : dict, optional
      Dictionary of additional keyword arguments to pass to func.
  
  inv_kw_args : dict, optional
      Dictionary of additional keyword arguments to pass to inverse_func.
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code: def _transform(self, X, y=None, func=None, kw_args=None):
      if self.validate:
          X = check_array(X, self.accept_sparse)
  
      if func is None:
          func = _identity
  
      return func(
          X,
          *((y,) if self.pass_y else ()),
          **(kw_args if kw_args else {}),
      )
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/preprocessing/_function_transformer.py
imports: from ..base import BaseEstimator, TransformerMixin
  from ..utils import check_array
label_desc: Use image node_id 5 for calling the FunctionTransformer obj's underlying member method code with example usage: obj._transform(X, y, func, kw_args) and returns: func