class_docstr: An example classifier which implements a 1-NN algorithm.
  
  For more information regarding how to build your own classifier, 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
  ----------
  X_ : ndarray, shape (n_samples, n_features)
      The input passed during :meth:`fit`.
  y_ : ndarray, shape (n_samples,)
      The labels passed during :meth:`fit`.
  classes_ : ndarray, shape (n_classes,)
      The classes seen at :meth:`fit`.
docstr: Train classifier on labeled data.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      The training input samples.
  y : array-like, shape (n_samples,)
      The target values. An array of int.
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y):
      """Train classifier on labeled data.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          The training input samples.
      y : array-like, shape (n_samples,)
          The target values. An array of int.
  
      Returns
      -------
      self : object
          Returns self.
      """
      # Check that X and y have correct shape
      X, y = check_X_y(X, y)
      # Store the classes seen during fit
      self.classes_ = unique_labels(y)
  
      self.X_ = X
      self.y_ = y
      # Return the classifier
      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 TemplateClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self