// CHECK: return %[[RESULT]] : tensor<1x4xi32>
  %sizes = "tf.Const"() {value = dense<[1, 4]> : tensor<2xi64>} : () -> (tensor<2xi64>)
  %0 = "tf.Slice"(%arg0, %arg1, %sizes) : (tensor<3x4xi32>, tensor<2xi64>, tensor<2xi64>) -> tensor<1x4xi32>
  func.return %0 : tensor<1x4xi32>
}

// CHECK-LABEL: slice_variable_start_negsize

  func.return %0 : tensor<i32>
}

// -----

// CHECK-LABEL: constant_tensor_vector
func.func @constant_tensor_vector(%arg0: vector<1x2xi32>) -> tensor<1x2xi32> {
  %0 = "tfr.constant_tensor"(%arg0) : (vector<1x2xi32>) -> tensor<1x2xi32>
  func.return %0 : tensor<1x2xi32>
}

// -----

// CHECK-LABEL: constant_tensor_array
func.func @constant_tensor_array() -> !tfr.tensor {

    %8 = stablehlo.convert %7 : (tensor<1x2xi32>) -> tensor<1x2xf32>
    %9 = stablehlo.convert %2 : (tensor<2x3xi8>) -> tensor<2x3xf32>
    %10 = stablehlo.dot_general %8, %9, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>

    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
    %2 = "tf.Const"() {value = dense<[7, 7, 3, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
    %3 = "tf.Const"() {value = dense<[[0, 0], [3, 3], [3, 3], [0, 0]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>
    %4 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

      rhs_contracting_dimensions = [0]
    >} : (
      tensor<?x?x6xi8>, tensor<6x8xi8>
    ) -> tensor<?x?x8xi32>
  %1 = chlo.broadcast_add %0, %zp_offset : (
      tensor<?x?x8xi32>, tensor<8xi32>) -> tensor<?x?x8xi32>
  %2 = chlo.broadcast_add %1, %bias : (
      tensor<?x?x8xi32>, tensor<8xi32>) -> tensor<?x?x8xi32>
  return %2 : tensor<?x?x8xi32>
}

// -----

// CHECK-LABEL: func @dot_general_add_add_static

  // CHECK: "tf.Const"() <{value = dense<> : tensor<0x3xi32>}> : () -> tensor<0x3xi32>
  // CHECK-NOT: tf.TensorListStack
  %1 = "tf.TensorListStack"(%0, %arg0) : (tensor<!tf_type.variant<tensor<3xi32>>>, tensor<1xi32>) -> tensor<0x3xi32>
  func.return %1 : tensor<0x3xi32>
}

// -----

// CHECK-LABEL: func @tensorlistGetItem

  %cst = "tf.Const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %cst_0 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %cst_1 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
  %cst_2 = "tf.Const"() {value = dense<4> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %0 = "tf.SpaceToBatchND"(%arg0, %cst_1, %cst_2) : (tensor<1x128x3xf32>, tensor<1xi32>, tensor<1x2xi32>) -> tensor<2x68x3xf32>

module attributes {tf.versions = {producer = 888 : i32}} {
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

// fixed upstream).
func.func @broadcast_add(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi32> {
  // CHECK-NEXT: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT: mhlo.add %[[LHS_BCAST]], %arg1
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi32>
  func.return %0: tensor<1x2xi32>
}

// CHECK-LABEL: func @broadcast_multi_dim_add

func.func @my_cast(%arg0: tensor<2x3xf32>) -> tensor<2x3xi32> {
  %0 = "tf.MyCast"(%arg0) {Tout=i32} : (tensor<2x3xf32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-NEXT: %[[CAST:.*]] = "tf.RiscCast"(%arg0) {Tout = i32} : (tensor<2x3xf32>) -> tensor<*xi32>
// CHECK-NEXT: %[[ES:.*]] = "tf.EnsureShape"(%[[CAST]]) <{shape = #tf_type.shape<2x3>}> : (tensor<*xi32>) -> tensor<2x3xi32>
// CHECK-NEXT: return %[[ES]] : tensor<2x3xi32>
}