host_compute_core = [], input_sharding_configuration = ["\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00"], num_cores_per_replica = 1 : i64, output_sharding_configuration = ["\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00", "\08\01\1A\01\01\22\01\00"], step_marker_location...

      NumDynamicShapes = 0 : i64,
      // The metadata encodes 2 parameter and two return values.
      metadata = "\0A\0E\08\01\18\01\22\08\08\01\1A\01\01\22\01\00\0A \08\01\12\10\12\02\08\03\12\02\08\03\12\02\08\01\12\02\08 \18\01\22\08\08\01\1A\01\01\22\01\00\12\0A\0A\08\08\01\1A\01\01\22\01\00\12\0A\0A\08\08\01\1A\01\01\22\01\00\18\02 \01",
      mlir_module = "..."} : () -> (tensor<!tf_type.string>, tensor<2x!tf_type.string>)

    // CHECK:      "tf._TPUCompil...

  // CHECK: input_sharding_configuration
  // CHECK-SAME: ["\08\01\1A\01\01\22\01\00"]
  // CHECK: output_sharding_configuration
  // CHECK-SAME: ["\08\01\1A\01\01\22\01\00"]
  func.return
}

// CHECK-LABEL: func @func_without_sharding
// CHECK-SAME: (%{{[a-z0-9]+}}: tensor<*xi32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"})
// CHECK-SAME: -> (tensor<*xi32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"})

        given:
        def consoleOutput = """\u001B[1A\u001B[1m> Connecting to Daemon\u001B[m\u001B[22D
\u001B[1A\u001B[90m> IDLE\u001B[39m\u001B[0K\u001B[6D
\u001B[1A\u001B[1m<-------------> 0% CONFIGURING [0s]\u001B[m\u001B[35D\u001B[1B\u001B[1m> root project\u001B[m\u001B[14D

    func.return %arg0, %arg1, %arg2 : tensor<128x10xf32>, tensor<10x1024xf32>, tensor<128x1024xf32>
  }
}

// The following xla::OpSharding protos are used:
//  Serialized string:
//   "\08\03\1A\02\01\02\22\02\00\01"
//  Proto debug string:
//   type: OTHER

              metadata = "\0A\0E\08\01\18\01\22\08\08\01\1A\01\01\22\01\00\0A \08\01\12\10\12\02\08\03\12\02\08\03\12\02\08\01\12\02\08 \18\01\22\08\08\01\1A\01\01\22\01\00\0A\0E\08\01\18\01\22\08\08\01\1A\01\01\22\01\00\12\0A\0A\08\08\01\1A\01\01\22\01\00\12\0A\0A\08\08\01\1A\01\01\22\01\00\12\0A\0A\08\08\01\1A\01\01\22\01\00\18\02 \01",

00000110  b6 d7 49 a6 b5 68 1a 41  03 56 6b dc 5a 89        |..I..h.A.Vk.Z.|
>>> Flow 2 (server to client)
00000000  16 03 03 00 5d 02 00 00  59 03 03 1c ad ae e9 64  |....]...Y......d|
00000010  35 80 c6 04 f5 69 05 bb  5c 38 78 d3 99 2f ad e6  |5....i..\8x../..|
00000020  56 20 d4 79 89 1a 51 54  14 ac 6d 20 06 6d 1a bd  |V .y..QT..m .m..|

  func.func @main(%arg0: tensor<128x10xf32> {mhlo.sharding = "\08\03\1A\02\01\02\22\02\00\01"}, %arg1: tensor<10x1024xf32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"}, %arg2: tensor<128x1024xf32> {mhlo.sharding = ""}) {
    func.return
  }
}

// The following xla::OpSharding protos are used:
//  Serialized string:
//   "\08\03\1A\02\01\02\22\02\00\01"
//  Proto debug string:
//   type: OTHER

        Stage.from(1, "9") < Stage.from(2, "1")
        Stage.from(2, "1") > Stage.from(1, "9")

        Stage.from(1, "1") < Stage.from(1, "1a")
        Stage.from(1, "1a") > Stage.from(1, "1")

        Stage.from(1, "1a") < Stage.from(1, "1b")
        Stage.from(1, "1b") > Stage.from(1, "1a")

        Stage.from(1, "1c") < Stage.from(1, "2b")
        Stage.from(1, "2b") > Stage.from(1, "1c")
    }