docstr: Fits a transformer using the SFrame `data`.
  
  Parameters
  ----------
  data : SFrame
      The data used to fit the transformer.
  
  Returns
  -------
  self (A fitted object)
  
  See Also
  --------
  transform, fit_transform
code: def fit(self, data):
      """
      Fits a transformer using the SFrame `data`.
  
      Parameters
      ----------
      data : SFrame
          The data used to fit the transformer.
  
      Returns
      -------
      self (A fitted object)
  
      See Also
      --------
      transform, fit_transform
      """
  
      self._setup_from_data(data)
      self.transform_chain.fit(data)
  
      self.__proxy__.update({"fitted": True})
      return self
  
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 5 for calling the AutoVectorizer obj's underlying member method code with example usage: obj.fit(data) and returns: self