class_docstr: A class for feature agglomeration via the transform interface
docstr: Transform a new matrix using the built clustering
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features] or [n_features]
      A M by N array of M observations in N dimensions or a length
      M array of M one-dimensional observations.
  
  Returns
  -------
  Y : array, shape = [n_samples, n_clusters] or [n_clusters]
      The pooled values for each feature cluster.
docstr_tok: ["Transform","a","new","matrix","using","the","built","clustering","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","[","n_samples",",","n_features","]","or","[","n_features","]","A","M","by","N","array","of","M","observations","in","N","dimensions","or","a","length","M","array","of","M","one-dimensional","observations",".","Returns","--","--","--","-","Y",":","array",",","shape","=","[","n_samples",",","n_clusters","]","or","[","n_clusters","]","The","pooled","values","for","each","feature","cluster","."]
code: def transform(self, X):
      """
      Transform a new matrix using the built clustering
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features] or [n_features]
          A M by N array of M observations in N dimensions or a length
          M array of M one-dimensional observations.
  
      Returns
      -------
      Y : array, shape = [n_samples, n_clusters] or [n_clusters]
          The pooled values for each feature cluster.
      """
      check_is_fitted(self, "labels_")
  
      pooling_func = self.pooling_func
      X = check_array(X)
      nX = []
      if len(self.labels_) != X.shape[1]:
          raise ValueError(
              "X has a different number of features than "
              "during fitting."
          )
  
      for l in np.unique(self.labels_):
          nX.append(pooling_func(X[:, self.labels_ == l], axis=1))
      return np.array(nX).T
  
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 1 for calling the AgglomerationTransform obj's underlying member method code with example usage: obj.transform(X) and returns: np