docstr: Construct `num_points` 2D points arranged equiangularly about the origin.
  
  Use a magnitude multiplier of 1.1 to test that the cosine similarity
  function is ignoring the magnitutes of the vectors (i.e., is normalizing
  the vectors before taking their dot product).
  
  Args:
    num_points: the number of points to create. The first is `(1.0, 0.0)`.
  
  Returns:
    A pair containing the resulting embeddings and the cosine similarity
    between adjacent points.
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code: def _build_test_embeddings(self, num_points):
      """Construct `num_points` 2D points arranged equiangularly about the origin.
  
      Use a magnitude multiplier of 1.1 to test that the cosine similarity
      function is ignoring the magnitutes of the vectors (i.e., is normalizing
      the vectors before taking their dot product).
  
      Args:
        num_points: the number of points to create. The first is `(1.0, 0.0)`.
  
      Returns:
        A pair containing the resulting embeddings and the cosine similarity
        between adjacent points.
      """
      rotation = cmath.rect(1.1, 2 * cmath.pi / num_points)
      vector = 1.0 + 0.0j
      embeddings = []
      for node_id in range(num_points):
          embedding = tf.train.Example()
          embedding.features.feature["id"].bytes_list.value.append(
              "id_{}".format(node_id).encode("utf8")
          )
          values = embedding.features.feature[
              "embedding"
          ].float_list.value
          values.append(vector.real)
          values.append(vector.imag)
          embeddings.append(text_format.MessageToString(embedding))
          vector = vector * rotation
      # Cosine similarity between adjacent points = 0.951057 for 20 points.
      adjacent_similarity = round(rotation.real / abs(rotation), 6)
      return (embeddings, adjacent_similarity)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import cmath
  from absl.testing import absltest
  from neural_structured_learning.tools import build_graph
  from neural_structured_learning.tools import graph_utils
  import six
  import tensorflow
  from google.protobuf import text_format
label_desc: Use image node_id 8 for calling the BuildGraphTest obj's underlying member method code with example usage: obj._build_test_embeddings(num_points) and returns: embeddings,  adjacent_similarity