class_docstr: An example transformer that returns the element-wise square root.
  
  For more information regarding how to build your own transformer, read more
  in the :ref:`User Guide <user_guide>`.
  
  Parameters
  ----------
  demo_param : str, default='demo'
      A parameter used for demonstation of how to pass and store paramters.
  
  Attributes
  ----------
  n_features_ : int
      The number of features of the data passed to :meth:`fit`.
docstr: Train function for a transformer.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      The training input samples.
  y : None
      There is no need of a target in a transformer, yet the pipeline API
      requires this parameter.
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y=None):
      """Train function for a transformer.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          The training input samples.
      y : None
          There is no need of a target in a transformer, yet the pipeline API
          requires this parameter.
  
      Returns
      -------
      self : object
          Returns self.
      """
      X = check_array(X, accept_sparse=True)
  
      self.n_features_ = X.shape[1]
  
      # Return the transformer
      return self
  
imports: from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin
  from sklearn.metrics import euclidean_distances
  from sklearn.utils.multiclass import unique_labels
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  import geomstats.backend
label_desc: Use image node_id 2 for calling the TemplateTransformer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self