class_docstr: A class for feature agglomeration via the transform interface
docstr: Inverse the transformation.
  Return a vector of size nb_features with the values of Xred assigned
  to each group of features
  
  Parameters
  ----------
  Xred : array-like, shape=[n_samples, n_clusters] or [n_clusters,]
      The values to be assigned to each cluster of samples
  
  Returns
  -------
  X : array, shape=[n_samples, n_features] or [n_features]
      A vector of size n_samples with the values of Xred assigned to
      each of the cluster of samples.
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code: def inverse_transform(self, Xred):
      """
      Inverse the transformation.
      Return a vector of size nb_features with the values of Xred assigned
      to each group of features
  
      Parameters
      ----------
      Xred : array-like, shape=[n_samples, n_clusters] or [n_clusters,]
          The values to be assigned to each cluster of samples
  
      Returns
      -------
      X : array, shape=[n_samples, n_features] or [n_features]
          A vector of size n_samples with the values of Xred assigned to
          each of the cluster of samples.
      """
      check_is_fitted(self, "labels_")
  
      unil, inverse = np.unique(self.labels_, return_inverse=True)
      return Xred[..., inverse]
  
imports: import numpy
  from ..base import TransformerMixin
  from ..utils import check_array
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling the AgglomerationTransform obj's underlying member method code with example usage: obj.inverse_transform(Xred) and returns: Xred