docstr: Transform X in the existing embedded space back into the input
  data space and return that transformed output.
  Parameters
  ----------
  X : array, shape (n_samples, n_components)
      New points to be inverse transformed.
  Returns
  -------
  X_new : array, shape (n_samples, n_features)
      Generated data points new data in data space.
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code: def inverse_transform(self, X):
      """Transform X in the existing embedded space back into the input
      data space and return that transformed output.
      Parameters
      ----------
      X : array, shape (n_samples, n_components)
          New points to be inverse transformed.
      Returns
      -------
      X_new : array, shape (n_samples, n_features)
          Generated data points new data in data space.
      """
      if self.parametric_reconstruction:
          return self.decoder.predict(
              np.asanyarray(X),
              batch_size=self.batch_size,
              verbose=self.verbose,
          )
      else:
          return super().inverse_transform(X)
  
imports: import numpy
  from umap import UMAP
  from warnings import warn, catch_warnings, filterwarnings
  from numba import TypingError
  import os
  from umap.spectral import spectral_layout
  from sklearn.utils import check_random_state
  import codecs, pickle
  from sklearn.neighbors import KDTree
  import sys
label_desc: Use image node_id 5 for calling the ParametricUMAP obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: self, super