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: Train estimator on labeled data.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      The training input samples.
  y : array-like, shape (n_samples,) or (n_samples, n_outputs)
      The target values (class labels in classification, real numbers in
      regression).
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y):
      """Train estimator on labeled data.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          The training input samples.
      y : array-like, shape (n_samples,) or (n_samples, n_outputs)
          The target values (class labels in classification, real numbers in
          regression).
  
      Returns
      -------
      self : object
          Returns self.
      """
      X, y = check_X_y(X, y, accept_sparse=True)
      self.is_fitted_ = True
      # `fit` should always return `self`
      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 TemplateEstimator obj's underlying member method code with example usage: obj.fit(X, y) and returns: self