class_docstr: Meta-transformer for selecting categorical features and transform them using OneHotEncoder.
  
  Parameters
  ----------
  
  threshold : int, default=10
      Maximum number of unique values per feature to consider the feature
      to be categorical.
  
  minimum_fraction: float, default=None
      Minimum fraction of unique values in a feature to consider the feature
      to be categorical.
class_docstr_tok: ["Meta-transformer","for","selecting","categorical","features","and","transform","them","using","OneHotEncoder",".","Parameters","--","--","--","--","--","threshold",":","int",",","default=10","Maximum","number","of","unique","values","per","feature","to","consider","the","feature","to","be","categorical",".","minimum_fraction",":","float",",","default=None","Minimum","fraction","of","unique","values","in","a","feature","to","consider","the","feature","to","be","categorical","."]
docstr: Do nothing and return the estimator unchanged
  This method is just there to implement the usual API and hence
  work in pipelines.
  Parameters
  ----------
  X : array-like
docstr_tok: ["Do","nothing","and","return","the","estimator","unchanged","This","method","is","just","there","to","implement","the","usual","API","and","hence","work","in","pipelines",".","Parameters","--","--","--","--","--","X",":","array-like"]
code: def fit(self, X, y=None):
      """Do nothing and return the estimator unchanged
      This method is just there to implement the usual API and hence
      work in pipelines.
      Parameters
      ----------
      X : array-like
      """
      X = check_array(X, accept_sparse="csr")
      return self
  
file_path: tpot/tpot/builtins/feature_transformers.py
imports: import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
  from sklearn.decomposition import PCA
  from .one_hot_encoder import OneHotEncoder, auto_select_categorical_features, _X_selected
label_desc: Use image node_id 2 for calling the CategoricalSelector obj's underlying member method code with example usage: obj.fit(X, y) and returns: self