class_docstr: LabelEncoder wrapper with TransformerMixin. Allows LabelEncoder
  to work with a Pipeline.
class_docstr_tok: ["LabelEncoder","wrapper","with","TransformerMixin",".","Allows","LabelEncoder","to","work","with","a","Pipeline","."]
docstr: Fit the LabelEncoder 
docstr_tok: ["Fit","the","LabelEncoder"]
code: def fit(self, X, y=None):
      """Fit the LabelEncoder"""
      self.le = LabelEncoder()
      self.le.fit(X)
      return self
  
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 for calling the LabelEncoderPipe obj's underlying member method code with example usage: obj.fit(X, y) and returns: self