code: def create(dataset, transformers):
      """
      Create a Transformer object to transform data for feature engineering.
  
      Parameters
      ----------
      dataset : SFrame
          The dataset to use for training the model.
  
      transformers: Transformer  | list[Transformer]
          An Transformer or a list of Transformers.
  
      See Also
      --------
      turicreate.toolkits.feature_engineering._feature_engineering._TransformerBase
  
      Examples
      --------
  
      .. sourcecode:: python
  
          # Create data.
          >>> sf = turicreate.SFrame({'a': [1,2,3], 'b' : [2,3,4]})
  
          >>> from turicreate.feature_engineering import FeatureHasher, \
                                                 QuadraticFeatures, OneHotEncoder
  
          # Create a single transformer.
          >>> encoder = turicreate.feature_engineering.create(sf,
                                   OneHotEncoder(max_categories = 10))
  
          # Create a chain of transformers.
          >>> chain = turicreate.feature_engineering.create(sf, [
                                      QuadraticFeatures(),
                                      FeatureHasher()
                                    ])
  
          # Create a chain of transformers with names for each of the steps.
          >>> chain = turicreate.feature_engineering.create(sf, [
                                      ('quadratic', QuadraticFeatures()),
                                      ('hasher', FeatureHasher())
                                    ])
  
  
      """
      err_msg = "The parameters 'transformers' must be a valid Transformer object."
      cls = transformers.__class__
  
      _raise_error_if_not_sframe(dataset, "dataset")
  
      # List of transformers.
      if cls == list:
          transformers = TransformerChain(transformers)
      # Transformer.
      else:
          if not issubclass(cls, TransformerBase):
              raise TypeError(err_msg)
      # Fit and return
      transformers.fit(dataset)
      return transformers
  
file_path: turicreate/src/python/turicreate/toolkits/_feature_engineering/__init__.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from ._feature_engineering import Transformer
  from ._feature_engineering import _SampleTransformer
  from ._feature_engineering import TransformerBase
  from ._transformer_chain import TransformerChain
  from ._tokenizer import Tokenizer
  from ._tfidf import TFIDF
  from ._bm25 import BM25
  from ._word_counter import WordCounter
  from ._ngram_counter import NGramCounter
  from ._word_trimmer import RareWordTrimmer
  from ._transform_to_flat_dictionary import TransformToFlatDictionary
  from ._autovectorizer import AutoVectorizer
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
label_desc: Use image node_id 1 for calling a global function with example usage: create(dataset, transformers) and returns: transformers