class_docstr: Convert a sparse array into a dense array.
  
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/preprocessing/DenseTransformer/
docstr: Return a dense version of the input array.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  y : array-like, shape = [n_samples] (default: None)
  
  Returns
  ---------
  X_dense : dense version of the input X array.
docstr_tok: ["Return","a","dense","version","of","the","input","array",".","Parameters","--","--","--","--","--","X",":","{","array-like",",","sparse","matrix","}",",","shape","=","[","n_samples",",","n_features","]","Training","vectors",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","y",":","array-like",",","shape","=","[","n_samples","]","(","default",":","None",")","Returns","--","--","--","--","-","X_dense",":","dense","version","of","the","input","X","array","."]
code: def fit_transform(self, X, y=None):
      """Return a dense version of the input array.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples and
          n_features is the number of features.
      y : array-like, shape = [n_samples] (default: None)
  
      Returns
      ---------
      X_dense : dense version of the input X array.
  
      """
      return self.transform(X=X, y=y)
  
file_path: mlxtend/mlxtend/preprocessing/dense_transformer.py
imports: from scipy.sparse import issparse
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 4 for calling the DenseTransformer obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self