// 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

// CHECK:           %[[VAL_14:.*]] = "tf.Abs"(%[[VAL_1]]) : (tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_15:.*]] = "tf.Div"(%[[VAL_13]], %[[VAL_14]]) : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_16:.*]] = "tf.Select"(%[[VAL_6]], %[[VAL_7]], %[[VAL_15]]) : (tensor<2x3xi1>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>

// -----

// Tests that int constants are not folded.

// CHECK-LABEL: transpose_int
func.func @transpose_int() -> tensor<3x2xi32> {
  %0 = stablehlo.constant dense<0> : tensor<2x3xi32>
  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xi32>) -> tensor<3x2xi32>
  return %1 : tensor<3x2xi32>
}
// CHECK: transpose

// -----

// Tests that transposing an argument cannot be folded.

// CHECK-LABEL: transpose_arg

  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>

}

// -----

// Valid Pack operation.
func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x8xf32>) -> tensor<*xf32> {
  %0 = "tf.Pack"(%arg0, %arg1) {axis = 1 : i64} : (tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}


// -----

func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x2xf32>) -> tensor<*xf32> {

}

func.func @addN(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>, %arg2: tensor<2x3xi32>) -> tensor<2x3xi32> {
  %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-LABEL: addN
// CHECK:  "tfl.add_n"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:  return
}

    func.return
  }

  // CHECK: private @__inference_Dataset_map_lambda_96150(%arg0: tensor<64xi32>
  // CHECK-SAME: %arg1: tensor<69xi32>, %arg2: tensor<73xi32>, %arg3: tensor<12xi64>
  // CHECK-SAME: %arg8: tensor<64xi32>, %arg9: tensor<64xi32>
  // CHECK-SAME: %arg12: tensor<10xi32>)
  // CHECK-SAME: -> (tensor<64xi32>, tensor<69xi32>, tensor<10xi32>)

    %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>

    %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<*x!tf_type.resource<tensor<2x8xi32>>>
    %1 = "tf.VarHandleOp"() {container = "c", shared_name = "w"} : () -> tensor<*x!tf_type.resource<tensor<2xi32>>>
    %2 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>