code: def test_single_feature_lookup_1D(self, sigma_dimension):
      emb_dim = 5 + sigma_dimension
      config = test_util.default_de_config(emb_dim, [1] * emb_dim)
      fea_embed = sparse_features.SparseFeatureEmbedding(
          config,
          {"fea": (5, sigma_dimension)},
          op_name="single_feature_%d" % sigma_dimension,
          service_address=self._kbs_address,
      )
      embed, _, _, embed_map = fea_embed.lookup(["input1", "input2"])
      if sigma_dimension > 0:
          self.assertEqual((2, 5), embed.shape)
      else:
          self.assertAllClose([[1] * 5, [1] * 5], embed)
      self.assertEqual(["fea"], list(embed_map.keys()))
      self.assertEqual((2, 5), embed_map["fea"].shape)
      self.assertEqual(["fea"], list(fea_embed._variable_map.keys()))
  
imports: from absl.testing import parameterized
  from research.carls.models.caml import sparse_features
  from research.carls.testing import test_util
  import numpy
  import tensorflow
label_desc: Use image node_id 7 for calling the FeatureEmbeddingTest obj's underlying member method code with example usage: obj.test_single_feature_lookup_1D(sigma_dimension) without return types