code: def _transform_one(transformer, weight, X):
      res = transformer.transform(X)
      # if we have a weight for this transformer, multiply output
      if weight is None:
          return res
      return res * weight
  
imports: import ast
  import numpy
  from pandas import DataFrame
  from scipy import sparse
  from copy import copy
  from joblib import Parallel, delayed
  from sklearn.pipeline import FeatureUnion
  from sklearn.base import TransformerMixin, BaseEstimator
  from .base import _parse_partitions
label_desc: Use image node_id 1 for calling a global function with example usage: _transform_one(transformer, weight, X) and returns: unknown, res