docstr: Transform X into the existing embedded space and return that
  transformed output.
  Parameters
  ----------
  X : array, shape (n_samples, n_features)
      New data to be transformed.
  Returns
  -------
  X_new : array, shape (n_samples, n_components)
      Embedding of the new data in low-dimensional space.
docstr_tok: ["Transform","X","into","the","existing","embedded","space","and","return","that","transformed","output",".","Parameters","--","--","--","--","--","X",":","array",",","shape","(","n_samples",",","n_features",")","New","data","to","be","transformed",".","Returns","--","--","--","-","X_new",":","array",",","shape","(","n_samples",",","n_components",")","Embedding","of","the","new","data","in","low-dimensional","space","."]
code: def transform(self, X):
      """Transform X into the existing embedded space and return that
      transformed output.
      Parameters
      ----------
      X : array, shape (n_samples, n_features)
          New data to be transformed.
      Returns
      -------
      X_new : array, shape (n_samples, n_components)
          Embedding of the new data in low-dimensional space.
      """
      if self.parametric_embedding:
          return self.encoder.predict(
              np.asanyarray(X),
              batch_size=self.batch_size,
              verbose=self.verbose,
          )
      else:
          warn(
              "Embedding new data is not supported by ParametricUMAP. \
                  Using original embedder."
          )
          return super().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 4 for calling the ParametricUMAP obj's underlying member method code with example usage: obj.transform(X) and returns: self, super