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: Transform input data.
  
  Parameters
  ----------
  X : {array-like, sparse-matrix}, shape (n_samples, n_features)
      The input samples.
  
  Returns
  -------
  X_transformed : array, shape (n_samples, n_features)
      The array containing the element-wise square roots of the values
      in ``X``.
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code: def transform(self, X):
      """Transform input data.
  
      Parameters
      ----------
      X : {array-like, sparse-matrix}, shape (n_samples, n_features)
          The input samples.
  
      Returns
      -------
      X_transformed : array, shape (n_samples, n_features)
          The array containing the element-wise square roots of the values
          in ``X``.
      """
      # Check is fit had been called
      check_is_fitted(self, "n_features_")
  
      # Input validation
      X = check_array(X, accept_sparse=True)
  
      # Check that the input is of the same shape as the one passed
      # during fit.
      if X.shape[1] != self.n_features_:
          raise ValueError(
              "Shape of input is different from what was seen"
              "in `fit`"
          )
      return gs.sqrt(X)
  
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 3 for calling the TemplateTransformer obj's underlying member method code with example usage: obj.transform(X) and returns: gs