%0 = tf_executor.graph {
    %1:3 = tf_executor.island {
      %2:2 = tf_device.replicate([%arg0, %arg0] as %ri0: tensor<i1>) {n = 2 : i32} {
        tf_device.return %ri0 : tensor<i1>
      }
      tf_executor.yield %2#0, %2#1 : tensor<i1>, tensor<i1>
    }
    tf_executor.fetch %1#1 : tensor<i1>
  }
  func.return
}

// CHECK: {{%.*}}, [[REPLICA_0_CONTROL:%.*]] = tf_executor.island

  // expected-remark@above {{ID: 7}}
  "tf.PwStreamResults"(%arg0) {_callback_id = -2694175233261920887 : i64, _controller_address = "[2002:afb:afb::]:10004", _has_manual_control_dependencies = true, _model_name = "test", device = "/device/CPU", names = ["begin"]} : (tensor<i32>) -> ()
  // expected-remark@above {{ID: 0}}
  // expected-remark@above {{Successors: {4}}}

63\371\257!\353?:\346\250\252\025\'\344?4\3439\177\243*\341?\3247P\2531\004\324?\230\247B\035\250\362\342?P\0223[dG\351?\021\340\300\202L\313\356?d\241O\332l\232\322?R\020)C\351\327\341?\030\266ci\230g\274?\000zL\316bp\305?\377fO \033\361\347?\366\227E\301\tp\342?DoO\222\360L\346?\034\002I\203\320\007\300?\270,\377&}q\274?\346\266\247\251%D\353?\000\300\nj\375i\022?R\214\277\267\246\353\321?\356\257rD\250t\340?\235\220\270c\216_\343?\000.\273\357\337\340\355?\224\010\215r\301c\337?cq\316\217\221...

    output = input / (bias + alpha * sqr_sum) ** beta

For details, see [Krizhevsky et al., ImageNet classification with deep
convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
  }];

  let arguments = (ins
    Arg<TensorOf<[TF_Bfloat16, TF_Float16, TF_Float32]>, [{4-D.}]>:$input,