code: def fit_and_predict_test_data(self):
      smoke_data = []
  
      for p, metric, estimator in zip(
          self.input_dim, self.metrics, self.estimators
      ):
          y = gs.repeat(
              gs.expand_dims(metric._space.random_point(), 0),
              self.n_samples,
              axis=0,
          )
          X = gs.repeat(
              gs.reshape(
                  gs.linspace(0.0, 1.0, num=self.n_samples), (-1, 1)
              ),
              p,
              axis=1,
          )
          datum = dict(estimator=estimator, X=X, y=y)
          smoke_data.append(datum)
  
      return smoke_data
  
imports: import geomstats.backend
  from geomstats.geometry.stratified.graph_space import ExhaustiveAligner, GraphSpace, _GeodesicToPointAligner
  from geomstats.learning.aac import _AACGGPCA, _AACFrechetMean, _AACRegression
  from geomstats.test.data import TestData
  from geomstats.test.test_case import np_backend
label_desc: Use image node_id 2 for calling the _TrivialRegressionData obj's underlying member method code with example usage: obj.fit_and_predict_test_data() and returns: smoke_data