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"]
docstr: Transform MultiLabelBinarizer 
docstr_tok: ["Transform","MultiLabelBinarizer"]
code: def transform(self, X, y=None):
      """Transform MultiLabelBinarizer"""
      # ignore unseen label warning
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          X_t = self.transformer.transform(X)
      if self.sparse_output:
          return sparse.csr_matrix(X_t)
      else:
          return X_t
  
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 3 for calling the MultihotEncoder obj's underlying member method code with example usage: obj.transform(X, y) and returns: sparse, X_t