class_docstr: Non-Negative Matrix Factorization (NMF) with projected gradient solver.
  
  This class is private and for comparison purpose only.
  It may change or disappear without notice.
class_docstr_tok: ["Non-Negative","Matrix","Factorization","(","NMF",")","with","projected","gradient","solver",".","This","class","is","private","and","for","comparison","purpose","only",".","It","may","change","or","disappear","without","notice","."]
code: def inverse_transform(self, W):
      check_is_fitted(self)
      return np.dot(W, self.components_)
  
imports: import numbers
  import sys
  import warnings
  from time import time
  import matplotlib.pyplot
  import numpy
  import pandas
  from joblib import Memory
  from sklearn.decomposition import NMF
  from sklearn.decomposition._nmf import _beta_divergence, _check_init, _initialize_nmf
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.utils import check_array
  from sklearn.utils._testing import ignore_warnings
  from sklearn.utils.extmath import safe_sparse_dot, squared_norm
  from sklearn.utils.validation import check_is_fitted, check_non_negative
label_desc: Use image node_id 4 for calling the _PGNMF obj's underlying member method code with example usage: obj.inverse_transform(W) and returns: np