Iterator<String> iterator = Lists.newArrayList("manny", "mo", "jack").iterator();
    assertEquals(1, Iterators.indexOf(iterator, Predicates.equalTo("mo")));
    assertEquals("jack", iterator.next());
    assertFalse(iterator.hasNext());
  }

  public void testIndexOf_consumedDataWithDuplicates() {
    Iterator<String> iterator = Lists.newArrayList("manny", "mo", "mo", "jack").iterator();

    Iterator<String> iterator = Lists.newArrayList("manny", "mo", "jack").iterator();
    assertEquals(1, Iterators.indexOf(iterator, Predicates.equalTo("mo")));
    assertEquals("jack", iterator.next());
    assertFalse(iterator.hasNext());
  }

  public void testIndexOf_consumedDataWithDuplicates() {
    Iterator<String> iterator = Lists.newArrayList("manny", "mo", "mo", "jack").iterator();

  }

  public void testIndexOf_twoElements() {
    List<String> list = Lists.newArrayList("mary", "bob");
    assertEquals(0, Iterables.indexOf(list, Predicates.equalTo("mary")));
    assertEquals(1, Iterables.indexOf(list, Predicates.equalTo("bob")));
    assertEquals(-1, Iterables.indexOf(list, Predicates.equalTo("jack")));
  }

  }

  public void testIndexOf_twoElements() {
    List<String> list = Lists.newArrayList("mary", "bob");
    assertEquals(0, Iterables.indexOf(list, Predicates.equalTo("mary")));
    assertEquals(1, Iterables.indexOf(list, Predicates.equalTo("bob")));
    assertEquals(-1, Iterables.indexOf(list, Predicates.equalTo("jack")));
  }

    *   XLA reduction emitter is deterministic when the environment variable
        `TF_DETERMINISTIC_OPS` is set to "true" or "1". This extends
        deterministic `tf.nn.bias_add` back-prop functionality (and therefore
        also deterministic back-prop of bias-addition in Keras layers) to
        include when XLA JIT compilation is enabled.
    *   Fix problem, when running on a CUDA GPU and when either environment

      this.streamId = streamId
      this.errorCode = errorCode
    }

    override fun ping(
      ack: Boolean,
      payload1: Int,
      payload2: Int,
    ) {
      check(type == -1)
      type = Http2.TYPE_PING
      this.ack = ack
      this.payload1 = payload1
      this.payload2 = payload2
    }

    override fun goAway(
      lastGoodStreamId: Int,

    components.reserve(underlying_devices_.size());
    for (int j = 0; j < underlying_devices_.size(); ++j) {
      components.push_back(std::move(per_device_output_tensors[j][i]));
    }
    if (expected_output_shapes[i].IsFullyDefined()) {
      per_device_outputs.push_back(ParallelTensor::FromTensorHandles(
          *this, std::move(components),
          absl::Span<const int64_t>(expected_output_shapes[i].dim_sizes()),

    settings1[Settings.MAX_CONCURRENT_STREAMS] = maxConcurrentStreams
    peer.sendFrame().settings(settings1)
    peer.acceptFrame() // ACK
    peer.sendFrame().ping(false, 2, 0)
    peer.acceptFrame() // PING
    peer.play()

    // Play it back.
    val connection =
      Http2Connection.Builder(true, TaskRunner.INSTANCE)
        .socket(peer.openSocket())

        TFE_DeleteTensorHandle(outputs[i]);
      }
    }
    return status.status;
  }

  tensorflow::Status Pack(absl::Span<ImmediateExecutionTensorHandle*> handles,
                          ImmediateExecutionTensorHandle** result) override {
    TF_Status status;
    *result = tensorflow::unwrap(device_.pack(context_,
                                              tensorflow::wrap(handles.data()),

		h := xxh3.Hash(dst)
		dst = binary.LittleEndian.AppendUint32(dst, uint32(h))
	}
	return m.parent.send(m.ctx, dst)
}

// RequestStateless will send a single payload request and stream back results.
// req may not be read/written to after calling.
// TODO: Not implemented
func (m *muxClient) RequestStateless(h HandlerID, req []byte, out chan<- Response) {
	if m.init {