class_docstr: Wraps `MultiLabelBinarizer` in a pipeline safe transformer
  
  Args:
      sparse_output (bool): convert output to sparse matrix
class_docstr_tok: ["Wraps","`","MultiLabelBinarizer","`","in","a","pipeline","safe","transformer","Args",":","sparse_output","(","bool",")",":","convert","output","to","sparse","matrix"]
code: def __init__(self, sparse_output=False):
      self.transformer = MultiLabelBinarizer()
      self.sparse_output = sparse_output
  
imports: import warnings
  import pandas
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import MultiLabelBinarizer, FunctionTransformer, LabelEncoder, normalize
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn import feature_selection
label_desc: Use image node_id 1 to create a new MultihotEncoder object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = MultihotEncoder(sparse_output)