}
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);

        assertTrue(output1.contains("|"));

        // Test with less than 16 bytes
        baos.reset();
        byte[] data2 = { 0x41, 0x42, 0x43, 0x44 }; // "ABCD"
        Hexdump.hexdump(ps, data2, 0, 4);
        String output2 = baos.toString();

        assertNotNull(output2);
        assertTrue(output2.contains("00000:"));
        assertTrue(output2.contains(" 41 42 43 44"));

   *
   *   A =[[0, 1],[1, 0]]
   *   B =[[1, 0],[0, 1]]
   *
   *   output1, output2, output3 = two_adds_and_mm(A, B)
   *
   * We expect outputs:
   *
   *   output1 = [[1, 1],[1, 1]]
   *   output2 = [[2, 0],[0, 2]]
   *   output3 = [[2, 2],[2, 2]]
   *
   */

  // Build eager context.
  TFE_ContextOptions* opts = TFE_NewContextOptions();

        Thread.sleep(50);

        String output1 = thread.getOutput();
        boolean contains1 = thread.contains("concurrent1");

        thread.join(1000);

        String output2 = thread.getOutput();
        boolean contains2 = thread.contains("concurrent1");

        assertNotNull(output1);
        assertNotNull(output2);
        assertTrue(contains2);
    }

    @Test

  output_tensors->reserve(noutputs);
  for (int i = 0; i < noutputs; ++i) {
    Node* node = outputs[i].oper ? &outputs[i].oper->node : nullptr;
    int idx = outputs[i].index;
    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing output ", i, " from function '", fn_name,
        "'");
    TF_RETURN_WITH_CONTEXT_IF_ERROR(ValidateNonRefOutput(node, idx),

  VLOG(1) << "Running the dequeue op";
  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,

  ResetOutputValues();
  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

     *
     * Rough instructions to regenerate the test outputs and verify correctness:
     * - Temporarily change this test:
     * --- Comment out assertEquals.
     * --- System.out.println(input + " " + simplifyPath(input));
     * --- fail(). (If the test were to pass, its output would be hidden.)
     * - Run the test.
     * - Pull the relevant lines of output from the test into a testcases file.
     * - Test the output:

     *
     * Rough instructions to regenerate the test outputs and verify correctness:
     * - Temporarily change this test:
     * --- Comment out assertEquals.
     * --- System.out.println(input + " " + simplifyPath(input));
     * --- fail(). (If the test were to pass, its output would be hidden.)
     * - Run the test.
     * - Pull the relevant lines of output from the test into a testcases file.
     * - Test the output:

        }
    }

    /**
     * Outputs INFO information.
     *
     * @param message
     *            Message
     */
    public void info(final Object message) {
        if (isInfoEnabled()) {
            log.info(toString(message));
        }
    }

    /**
     * Outputs WARN information.
     *
     * @param message
     *            Message