}
    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"));

        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

  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());
}

        }
    }

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

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

        Consumer<ProcessBuilder> pbCall = pb -> {
            pb.redirectErrorStream(true);
        };

        List<String> outputs = new ArrayList<>();
        Consumer<String> outputCallback = output -> outputs.add(output);

        try {
            JobProcess jobProcess = processHelper.startProcess(sessionId, cmdList, pbCall, 1024, outputCallback);
            assertNotNull(jobProcess);

    }
  }

  sourceSets {
    val commonJvmAndroid =
      create("commonJvmAndroid") {
        dependsOn(commonMain.get())

        kotlin.srcDir(copyKotlinTemplates.map { it.outputs })
        kotlin.srcDir(generateIdnaMappingTable.map { it.outputs })

        dependencies {
          api(libs.square.okio)
          api(libs.kotlin.stdlib)

          compileOnly(libs.animalsniffer.annotations)
        }
      }

```

Use one of the following approaches to view job output:

View output in the Scala shell:

```
scala> counts.count()
364
```

To view the output from MinIO exit the Scala shell. View WordCount job status:

```
hadoop fs -ls s3a://testbucket/wordcount
```

The output should be similar to the following:

```
Found 3 items

// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* and
// '*num_retvals' should be set to the size of this array. It is an error if
// the size of 'retvals' is less than the number of outputs. This call sets
// *num_retvals to the number of outputs.
//
// If async execution is enabled, the call may simply enqueue the execution
// and return "non-ready" handles in `retvals`. Note that any handles contained