class_docstr: A template estimator to be used as a reference implementation.
  
  For more information regarding how to build your own estimator, read more
  in the :ref:`User Guide <user_guide>`.
  
  Parameters
  ----------
  demo_param : str, default='demo_param'
      A parameter used for demonstation of how to pass and store paramters.
docstr: Perform prediction.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      The training input samples.
  
  Returns
  -------
  y : ndarray, shape (n_samples,)
      Returns an array of ones.
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code: def predict(self, X):
      """Perform prediction.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          The training input samples.
  
      Returns
      -------
      y : ndarray, shape (n_samples,)
          Returns an array of ones.
      """
      X = check_array(X, accept_sparse=True)
      check_is_fitted(self, "is_fitted_")
      return gs.ones(X.shape[0], dtype=gs.int64)
  
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 TemplateEstimator obj's underlying member method code with example usage: obj.predict(X) and returns: gs