%17 = "tfl.reduce_prod"(%16, %cst_14) {keep_dims = true, tac.device = "CPU", tac.inference_type = "FLOAT"} : (tensor<2xi32>, tensor<1xi32>) -> tensor<1xi32>
      %18 = "tfl.reshape"(%arg1, %17) {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<?x?x!tf_type.string>, tensor<1xi32>) -> tensor<?x!tf_type.string>

  // CHECK-NEXT: %[[NEW_SIZE:.*]] = "tf.AddV2"(%[[ZERO]], %[[CONST1]]) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  %push = "tf.TensorListPushBack"(%id, %elem) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<f32>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK-NEXT: %[[COPY:.*]] = "tf.Identity"(%[[UPDATE]])
  // CHECK-NEXT: %[[CONST1_1:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>

// CHECK: key: "type"
// CHECK: type: DT_INT8

func.func @main(%arg0 : tensor<16xf32>) {
  tf_executor.graph {
    %1:2 = tf_executor.island wraps "tf.MlirPassthroughOp"(%arg0) {extra_type_attr = [tensor<5xi32>, tensor<16xf32>], Tinputs = [tensor<16xf32>], Toutputs = [tensor<16xf32>], mlir_module = ""} : (tensor<16xf32>) -> tensor<16xf32>
    tf_executor.fetch
  }
  func.return
}

func.func @plain() {
  tf_executor.graph {

//     (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<5xi32>
//   %shape = "tf.Const"() {value = dense<[5, 1]> : tensor<2xi32>}
//   %indices = "tf.Reshape"(%x, %shape) : (tensor<5xi32, tensor<2xi32) ->
//     tensor<5x1xi32>
//   "tf.TensorScatterUpdate"(%x, %indices, %updates) :
//     (tensor<5xi32>, tensor<5x1xi32>, tensor<5xi32>) -> tensor<5xi32>
//
class LowerInvertPermutationOp : public RewritePattern {
 public:

  // CHECK-DAG: %[[IND_SLICE0_START:.*]] = "tf.Const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK-DAG: %[[IND_SLICE0_SIZE:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK: %[[IND_SLICE0:.*]] = "tf.Slice"(%[[INDS]], %[[IND_SLICE0_START]], %[[IND_SLICE0_SIZE]]) : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>

// CHECK-NEXT:      %6 = "tfl.pseudo_const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
// CHECK-NEXT:      %7 = "tfl.unsorted_segment_prod"(%3, %4, %6) : (tensor<4xi32>, tensor<4xi32>, tensor<i32>) -> tensor<1xi32>
// CHECK-NEXT:      %8 = "tfl.unsorted_segment_prod"(%3, %5, %6) : (tensor<4xi32>, tensor<4xi32>, tensor<i32>) -> tensor<1xi32>

    %cst_11 = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
    %cst_12 = arith.constant dense<[0, 1, 3, 2]> : tensor<4xi32>
    %cst_18 = arith.constant dense<[0, 2, 1, 3]> : tensor<4xi32>
    %2112 = "tfl.transpose"(%arg0, %cst_11) : (tensor<1x4x1440x256xf32>, tensor<4xi32>) -> tensor<1x1440x256x4xf32>
    %2113 = "tfl.transpose"(%2112, %cst_18) : (tensor<1x1440x256x4xf32>, tensor<4xi32>) -> tensor<1x256x1440x4xf32>

    tf_device.return %1 : tensor<*xi32>
  }) : () -> (tensor<*xi32>)
  func.return %0: tensor<*xi32>
}



// Tests that resource subtype is correctly propagated when decomposing tf.ResourceGather.

// CHECK-LABEL: @decompose_resource_gather_op_subtype
func.func @decompose_resource_gather_op_subtype(%indices : tensor<5xi32>) -> tensor<2x5x16xi32> {

func.func @testBroadcastGradientArgs(%s0: tensor<4xi32>, %s1: tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>) {
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) : (tensor<4xi32>, tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>)
  func.return %r0, %r1 : tensor<1xi32>, tensor<0xi32>
}

// -----

func.func @testBroadcastGradientArgsIncompatibleInputType(%s0: tensor<4xi32>, %s1: tensor<4xi64>) -> (tensor<1xi32>, tensor<0xi32>) {

// CHECK:           %[[VAL_3:.*]] = "tf.LeftShift"(%[[VAL_1]], %[[VAL_0]]) : (tensor<4xi32>, tensor<1xi32>) -> tensor<4xi32>
// CHECK:           return %[[VAL_2]], %[[VAL_3]] : tensor<4xi32>, tensor<4xi32>
// CHECK:         }
func.func @broadcast_shift_left(%arg0: tensor<1xi32>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi32>) {