.isTrue();
      }
      ArrayList<Object> edgeList = new ArrayList<>(network.edges());

      assertThat(network.nodes()).hasSize(NUM_NODES);
      assertThat(network.edges()).hasSize(NUM_EDGES);
      AbstractNetworkTest.validateNetwork(network);

      Collections.shuffle(edgeList, gen);
      int numEdgesToRemove = gen.nextInt(NUM_EDGES);
      for (int i = 0; i < numEdgesToRemove; ++i) {
        Object edge = edgeList.get(i);

        assertNotNull(res.getExcidentEdges(v1), "no edges in the resulting graph after resolver");

        assertEquals(4, res.getVertices().size(), "wrong # of vertices in the resulting graph after resolver");
        assertEquals(
                2,
                res.getExcidentEdges(v1).size(),
                "wrong # of excident edges in the resulting graph entry after resolver");

        assertEquals(

            .isTrue();
      }
      ArrayList<Object> edgeList = new ArrayList<>(network.edges());

      assertThat(network.nodes()).hasSize(NUM_NODES);
      assertThat(network.edges()).hasSize(NUM_EDGES);
      AbstractNetworkTest.validateNetwork(network);

      Collections.shuffle(edgeList, gen);
      int numEdgesToRemove = gen.nextInt(NUM_EDGES);
      for (int i = 0; i < numEdgesToRemove; ++i) {
        Object edge = edgeList.get(i);

  mutex_lock l(graph->mu);
  std::vector<const tensorflow::Edge*> control_edges;
  for (const tensorflow::Edge* edge : op->node.in_edges()) {
    if (!edge->IsControlEdge()) continue;
    control_edges.push_back(edge);
  }
  for (const tensorflow::Edge* edge : control_edges) {
    graph->graph.RemoveControlEdge(edge);
  }
}

  ASSERT_EQ(1, TF_OperationNumInputs(neg));
  TF_Output neg_input = TF_OperationInput({neg, 0});
  EXPECT_EQ(scalar, neg_input.oper);
  EXPECT_EQ(0, neg_input.index);

  // Test that we can't see control edges involving the source and sink nodes.
  TF_Operation* control_ops[100];
  EXPECT_EQ(0, TF_OperationNumControlInputs(scalar));
  EXPECT_EQ(0, TF_OperationGetControlInputs(scalar, control_ops, 100));

    *   Removed need to put nsync/public on C include path when building custom
        ops.
    *   Add `tf.image.psnr`, `tf.image.ssim`, `tf.image.ssim_multiscale`,
        `tf.image.image_gradients`, `tf.image.sobel_edges`.
    *   Add links to https://js.tensorflow.org.
    *   Fix non-uniformity of orthogonal matrices.
    *   Fix bug where multi-image Estimator eval summaries were not displayed
        correctly.