%outputs_1, %control_1 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_0) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input1"} : (tensor<0xf32>) -> tensor<0xf32>

```python
import tensorflow as tf

@Composite(
    'FusedFullyConnected',
    inputs=['input_: T', 'filter_: T', 'bias: T'],
    attrs=['act: {"", "RELU", "RELU6", "TANH"} = ""'],
    derived_attrs=['T: {float, int8}'],
    outputs=['o: T'])
def _composite_fully_connected(input_, filter_, bias, act):
  res = tf.raw_ops.MatMul(
      a=input_, b=filter_, transpose_a=False, transpose_b=True)
  res = tf.raw_ops.Add(x=res, y=bias)

public class ProtwordsItem extends DictionaryItem {
    private final String input;

    private String newInput;

    public ProtwordsItem(final long id, final String input) {
        this.id = id;
        this.input = input;

        if (id == 0) {
            // create
            newInput = input;
        }
    }

    public String getNewInput() {
        return newInput;
    }

Unlike the TensorFlow Merge op, which requires inputs of some types to be
placed on the host, the _XlaMerge op can merge inputs of all types when
placed on the device. This prevents the need for copy operations, in
particular when an XLA cluster has int32 outputs. The _XlaMerge up does not
have a value_index output that identifies the chosen input.
)");

    return absl::OkStatus();
  }
};

// Computes:
//
// @tf.custom_gradient
// def f(input):
//   def grad(grads):
//     return grads[0]
//   return tf.exp(input), grad
// outputs = [f(inputs[0])]
Status ExpWithPassThroughGrad(AbstractContext* ctx,
                              absl::Span<AbstractTensorHandle* const> inputs,
                              absl::Span<AbstractTensorHandle*> outputs) {

  // The name assigned to the function which is the import result of the given
  // graph. If empty, a default one will be used.
  std::string graph_func_name;
  // Maps input node names to node data types and shapes.
  InputArrays inputs;
  // name:index strings for the data outputs.
  std::vector<string> outputs;
  // name strings for the control outputs.
  std::vector<string> control_outputs;

    @TaskAction fun action() { println(inputValue.get()) }
}

// tag::system-properties-task-inputs[]
tasks.register<PrintValue>("printProperty") {
    // Using the Gradle API, provides a lazy Provider<String> wired to a task input
    inputValue = providers.systemProperty("system")
}

  }

  private static void assertMessageDigestHashing(byte[] input, String algorithmName) {
    try {
      MessageDigest digest = MessageDigest.getInstance(algorithmName);
      assertEquals(
          HashCode.fromBytes(digest.digest(input)), ALGORITHMS.get(algorithmName).hashBytes(input));
      for (int bytes = 4; bytes <= digest.getDigestLength(); bytes++) {
        assertEquals(

                  const std::vector<Output>& inputs,
                  std::vector<Output>* outputs) {
  DCHECK(outputs != nullptr);
  DCHECK(outputs->empty());

  // The control dependency is analogous to that in CreateCond().
  Scope body_scope =
      scope.NewSubScope("body").WithControlDependencies(inputs[0]);
  TF_RETURN_IF_ERROR(body(body_scope, inputs, outputs));

  const size_t num_loop_vars = inputs.size();

  const llvm::MemoryBuffer* input =
      source_mgr->getMemoryBuffer(source_mgr->getMainFileID());
  std::string error;
  auto loc =
      mlir::FileLineColLoc::get(context, input->getBufferIdentifier(), 0, 0);

  // Parses input/output names from command line options.
  std::vector<std::string> inputs;
  std::vector<std::string> outputs;