"tf.XlaSendToHost"(%0) {key = "send_key"} : (tensor<i32>) -> ()

  // CHECK: _xla_original_oc_node_name = [[NODE_NAME3:.*]], _xla_token_input_nodes = {{\[}}[[NODE_NAME2]]{{\]}}
  %1 = "tf._XlaHostComputeMlir"(%0) {recv_key = "host_compute_channel_recv1", send_key = "host_compute_channel_send1", host_mlir_module = ""} : (tensor<i32>) -> (tensor<f32>)
  func.return
}

      %control = tf_executor.island wraps "tf.XlaHostCompute"() {_xla_original_oc_node_name = "Add", _xla_token_input_nodes = ["_xla_token_arg_node"], ancestors = [], cost_estimate_ns = 1024 : i64, key = "host_compute_channel_1_retvals", send_key = "", recv_key = "", shapes = [], tpu_core = 0 : i64} : () -> ()
      tf_executor.fetch
    }
    func.return
  }
}

// CHECK: op: "XlaHostCompute"

// CHECK:       attr {

    flow ops when necessary.

    For example, given the program

    ```mlir
      func @send_to_host(%arg0: tensor<i32>) {
        "tf.XlaSendToHost"(%arg0) {key = "send_key"} : (tensor<i32>) -> ()
        return
      }
    ```

    This might be legalized like this:

    ```mlir
      func @send_to_host(%arg0: tensor<i32>) {

    return
    }