class_docstr: A lightweight pipeline to chain transformers.
  
  Parameters
  ----------
  transformers : list
      A list of Transformer instances.
class_docstr_tok: ["A","lightweight","pipeline","to","chain","transformers",".","Parameters","--","--","--","--","--","transformers",":","list","A","list","of","Transformer","instances","."]
code: def __init__(self, transformers):
      self.transformers = list(transformers)
      for transformer in self.transformers:
          if not isinstance(transformer, Transformer):
              raise ValueError(
                  "Provided transformers should be a Transformer "
                  "instance. Got %s" % transformer
              )
  
imports: from __future__ import division
  import numpy
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.utils import column_or_1d
label_desc: Use image node_id 1 to create a new Pipeline object from inherited base classes: Transformer with example: obj = Pipeline(transformers)