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`.
code: def __init__(self, demo_param="demo"):
      self.demo_param = demo_param
  
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 1 to create a new TemplateClassifier object from inherited base classes: BaseEstimator, ClassifierMixin with example: obj = TemplateClassifier(demo_param)