code: def make_pipeline(*steps):
      """Construct a Pipeline from the given estimators.
  
      This is a shorthand for the Pipeline constructor; it does not require, and
      does not permit, naming the estimators. Instead, their names will be set
      to the lowercase of their types automatically.
  
      Examples
      --------
      >>> from sklearn.naive_bayes import GaussianNB
      >>> from sklearn.preprocessing import StandardScaler
      >>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
      ...     # doctest: +NORMALIZE_WHITESPACE
      Pipeline(steps=[('standardscaler',
                       StandardScaler(copy=True, with_mean=True, with_std=True)),
                      ('gaussiannb', GaussianNB(priors=None))])
  
      Returns
      -------
      p : Pipeline
      """
      return Pipeline(_name_estimators(steps))
  
imports: from collections import defaultdict
  from warnings import warn
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy import sparse
  from .base import BaseEstimator, TransformerMixin
  from .externals.joblib import Parallel, delayed
  from .externals import six
  from .utils import tosequence
  from .utils.metaestimators import if_delegate_has_method
label_desc: Use image node_id 2 for calling a global function with example usage: make_pipeline() and returns: Pipeline