code: def make_tansformers(generator, indexer, **kwargs):
      """Set up generators for the job to be performed"""
      transformers = [
          EvalTransformer(
              estimator=Pipeline(pipeline, return_y=True),
              name=preprocess_name,
              indexer=indexer,
              **kwargs,
          )
          for preprocess_name, pipeline in generator
      ]
      return transformers
  
file_path: mlens/mlens/model_selection/_base_functions.py
imports: from __future__ import division
  from collections import Counter
  from ..parallel import EvalLearner, EvalTransformer, Pipeline
  from ..utils.formatting import _assert_format
  from ..utils.checks import assert_valid_estimator
  from ..externals.sklearn.base import clone
label_desc: Use image node_id 7 for calling a global function with example usage: make_tansformers(generator, indexer) and returns: transformers