docstr: Fits and transforms the SFrame `data` using a fitted model.
  
  Parameters
  ----------
  data : SFrame
      The data  to be transformed.
  
  Returns
  -------
  A transformed SFrame.
  
  Returns
  -------
  out: SFrame
      A transformed SFrame.
  
  See Also
  --------
  fit, transform
code: def fit_transform(self, data):
      """
      Fits and transforms the SFrame `data` using a fitted model.
  
      Parameters
      ----------
      data : SFrame
          The data  to be transformed.
  
      Returns
      -------
      A transformed SFrame.
  
      Returns
      -------
      out: SFrame
          A transformed SFrame.
  
      See Also
      --------
      fit, transform
      """
  
      self._setup_from_data(data)
      ret = self.transform_chain.fit_transform(data)
      self.__proxy__.update({"fitted": True})
      return ret
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from .None import TransformerBase
  from .None import TransformerChain
  from .None import TransformToFlatDictionary
  from turicreate.toolkits._model import PythonProxy
  from turicreate.toolkits._model import ExposeAttributesFromProxy
  from turicreate.toolkits._internal_utils import _toolkit_repr_print
  from turicreate.toolkits._internal_utils import _precomputed_field
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
  from turicreate.util import _raise_error_if_not_of_type
  from turicreate.toolkits._internal_utils import _check_categorical_option_type
  from .None import _internal_utils
  from array import array
  import textwrap
  from turicreate import SFrame
  import turicreate
label_desc: Use image node_id 6 for calling the AutoVectorizer obj's underlying member method code with example usage: obj.fit_transform(data) and returns: ret