-> (tensor<?x2xi64>, tensor<?x2xi64>, tensor<?x!tf_type.string>, tensor<?xi64>, tensor<2xi64>, tensor<2xi64>, tensor<32xf32>, tensor<32xf32>)

  // CHECK: tfrt.return {{.*}}, [[outputs]]#0
  func.return %outputs#0 : tensor<?x2xi64>
}

// CHECK-LABEL: func @no_native

func.func @testIfExternIncompatibleCastTrivialTransform(%arg0: tensor<i1>, %arg1: tensor<2xi64>) -> tensor<2xf32> {
  // CHECK: %[[CAST:.*]] = "tf.Cast"(%arg1) <{Truncate = false}> : (tensor<2xi64>) -> tensor<*xf32>
  // CHECK: "tf.If"(%arg0, %[[CAST]]) <{else_branch = @testIf1Else, {{.+}} then_branch = @testIf1Then}
  %1 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<2xi64>) -> tensor<*xf32>
  %0 = "tf.IfRegion"(%arg0) ({

func.func @main(%arg0: tensor<10x8x9xf32>) -> tensor<2xi64> {
  %elem_shape = "tf.Const"() {value = dense<[8, 9]> : tensor<2xi32>} : () -> tensor<2xi32>
  %tl = "tf.TensorListFromTensor"(%arg0, %elem_shape) : (tensor<10x8x9xf32>, tensor<2xi32>) -> tensor<!tf_type.variant<tensor<8x9xf32>>>
  // CHECK: %[[SHAPE:.*]] = "tf.Const"() <{value = dense<[8, 9]> : tensor<2xi64>}> : () -> tensor<2xi64>

  // CHECK-DAG: %[[INP0:.*]] = "tf.Reshape"(%arg0, %[[SHAPE]]) : (tensor<2xf32>, tensor<2xi64>) -> tensor<1x2xf32>
  // CHECK-DAG: %[[ITEMS0:.*]] = "tf.Unpack"(%[[INP0]]) <{axis = 0 : i64}> : (tensor<1x2xf32>) -> tensor<2xf32>
  // CHECK-DAG: %[[INP1:.*]] = "tf.Reshape"(%arg1, %[[SHAPE]]) : (tensor<2x2x2xf32>, tensor<2xi64>) -> tensor<4x2xf32>

      }
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_BOOL
    }
  }
}

# CHECK-DAG: tf.IteratorGetNext{{.+}}-> (tensor<1x8xbf16>, tensor<2x?x16xf32>, tensor<*xf64>)
# CHECK-DAG: tf.IteratorGetNextSync{{.+}}-> (tensor<*xi16>, tensor<3x24xi32>, tensor<?x4x32xi64>)
# CHECK-DAG: tf.MultiDeviceIteratorGetNextFromShard{{.+}}-> (tensor<5x40xf16>, tensor<*xcomplex<f32>>, tensor<6x48x?xcomplex<f64>>)

//   [counter_lo, counter_hi, key]
//
//   RngReadAndSkip increments the 128 bit counter value by 256 * delta and
//   returns the original state value.
//
// For Threefry, the resource variable holds a tensor<2xi64> with the state:
//   [counter, key]
//
//   RngReadAndSkip increments the 64 bit counter value by 256 * delta and
//   returns a tensor<3xi64> value [counter, key, 0].
class DecomposeRngReadAndSkipOp : public RewritePattern {

  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ShapeOpI64
func.func @ShapeOpI64(%arg0 : tensor<576x72xf32>) -> tensor<2xi64> {
  %0 = "tfl.shape"(%arg0) : (tensor<576x72xf32>) -> tensor<2xi64>
  func.return %0 : tensor<2xi64>
  // CHECK: %[[CST:.*]] = arith.constant dense<[576, 72]> : tensor<2xi64>
  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ConstFoldStridedSlice

  func.func @map_outside_compilation_0_inputs(%arg0 : tensor<2xi64>) -> () {
    // CHECK:             "tf._XlaHostComputeMlir"()
    "tf_device.cluster"() ({
      %1 = "tf.OpA"(%arg0) {_xla_map_outside_compilation = "0", _xla_outside_compilation = "from_launch"} : (tensor<2xi64>) -> tensor<2xi64>
      "tf.OpB"(%1) : (tensor<2xi64>) -> ()
      tf_device.return

    %cst = arith.constant dense<[2, 100]> : tensor<2xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>
    func.return %1 : tensor<2x100xf32>
  }

//        limit_indices = dense<[4, 1]> : tensor<2xi64>,
//        start_indices = dense<0> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x1xf32>
// %1 = "mhlo.slice"(%input) {
//        limit_indices = dense<[4, 3]> : tensor<2xi64>,
//        start_indices = dense<[0, 1]> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>