docstr: Tests that the selected E-DPP size is equal to the sampled size.
docstr_tok: ["Tests","that","the","selected","E-DPP","size","is","equal","to","the","sampled","size","."]
code: def testSampleFromEDppSize(self):
      """Tests that the selected E-DPP size is equal to the sampled size."""
      n_points = 5
      edpp_indices = tf.constant(
          [
              [0] * n_points,  # Empty set.
              [0, 1, 0, 0, 1],
              [0, 0, 0, 1, 1],
              [1] * n_points,
          ],
          dtype=tf.int32,
      )
      eigenvectors = tf.random.uniform(
          [edpp_indices.shape[0], n_points, n_points],
          seed=test_util.test_seed(),
          dtype=self.param_dtype,
      )
      samples = self.evaluate(
          dpp_lib._sample_from_edpp(
              eigenvectors, edpp_indices, seed=test_util.test_seed()
          )
      )
      actual_sizes = np.sum(samples, axis=-1)
      expected_sizes = tf.reduce_sum(edpp_indices, axis=-1)
      self.assertAllEqual(actual_sizes, expected_sizes)
  
imports: import contextlib
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import dpp
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
label_desc: Use image node_id 6 for calling the _DppTest obj's underlying member method code with example usage: obj.testSampleFromEDppSize() without return types