code: def maximum_likelihood_fit_test_data(self):
      smoke_data = [
          dict(
              data=gs.array([1, 2, 3, 4]),
              expected=gs.array([4.26542805, 2.5]),
          ),
          dict(
              data=[[1, 2, 3, 4, 5], [1, 2, 3, 4]],
              expected=gs.array([[3.70164381, 3.0], [4.26542805, 2.5]]),
          ),
          dict(
              data=[gs.array([1, 2, 3, 4, 5]), gs.array([1, 2, 3, 4])],
              expected=gs.array([[3.70164381, 3.0], [4.26542805, 2.5]]),
          ),
          dict(
              data=[[1, 2, 3, 4]],
              expected=gs.array([4.26542805, 2.5]),
          ),
          dict(
              data=gs.array([1, 2, 3, 4]),
              expected=gs.array([4.26542805, 2.5]),
          ),
          dict(
              data=[gs.array([1, 2, 3, 4])],
              expected=gs.array([4.26542805, 2.5]),
          ),
          dict(
              data=[[1, 2, 3, 4, 5], gs.array([1, 2, 3, 4])],
              expected=gs.array([[3.70164381, 3.0], [4.26542805, 2.5]]),
          ),
      ]
      return self.generate_tests(smoke_data)
  
imports: import geomstats.backend
  from geomstats.test.data import TestData
  from ...test_geometry.data.base import VectorSpaceOpenSetTestData
  from ...test_geometry.data.riemannian_metric import RiemannianMetricTestData
  from .base import InformationManifoldMixinTestData
label_desc: Use image node_id 6 for calling the GammaDistributionsSmokeTestData obj's underlying member method code with example usage: obj.maximum_likelihood_fit_test_data() and returns: self