}

// CHECK-LABEL: @StridedSliceEllipsisAndNewAxisMaskBothSet
func.func @StridedSliceEllipsisAndNewAxisMaskBothSet(%arg0: tensor<6x7x8xf32>) -> tensor<2x1x7x8x1xf32> {
  %begin = arith.constant dense<0> : tensor<4xi32>
  %end = arith.constant dense<[2,3,4,5]> : tensor<4xi32>
  %step = arith.constant dense<1> : tensor<4xi32>

// Test scatter into existing tensor list.

// CHECK-LABEL: func @main
// CHECK-SAME: (%[[ARG0:.*]]: tensor<10x8x9xf32>, %[[ARG1:.*]]: tensor<5xi32>, %[[ARG2:.*]]: tensor<5x8x9xf32>) -> tensor<10x8x9xf32>
func.func @main(%arg0: tensor<10x8x9xf32>, %arg1: tensor<5xi32>, %arg2: tensor<5x8x9xf32>) -> tensor<10x8x9xf32> {
  %elem_shape = "tf.Const"() {value = dense<[8, 9]> : tensor<2xi32>} : () -> tensor<2xi32>

// CHECK:           %[[VAL_6:.*]] = "tf.Const"() <{value = dense<[5, 7, 9]> : tensor<3xi64>}> : () -> tensor<3xi64>
// CHECK:           %[[VAL_7:.*]] = "tf.BroadcastTo"(%[[VAL_5]], %[[VAL_6]]) : (tensor<1x7x1xi32>, tensor<3xi64>) -> tensor<5x7x9xi32>
// CHECK:           return %[[VAL_7]] : tensor<5x7x9xi32>
// CHECK:         }

  // CHECK:  return
}

func.func @pow(%arg0: tensor<2x1x3xf32>, %arg1: tensor<2x1x1xf32>) -> tensor<2x1x3xf32> {
  %0 = "tf.Pow"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  func.return %0 : tensor<2x1x3xf32>

  // CHECK-LABEL: pow
  // CHECK:  %[[pow:.*]] = tfl.pow(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  // CHECK:  return
}

// CHECK-SAME: (%[[A:.*]]: tensor<5x7xf32>, %[[B:.*]]: tensor<7x11xf32>)
func.func @matmul_notranspose(%a: tensor<5x7xf32>, %b: tensor<7x11xf32>) -> tensor<5x11xf32> {
  // CHECK: "mhlo.dot"(%[[A]], %[[B]])
  %0 = "tf.MatMul"(%a, %b) {transpose_a = false, transpose_b = false} : (tensor<5x7xf32>, tensor<7x11xf32>) -> tensor<5x11xf32>

  func.return %0 : tensor<5x11xf32>
}

// -----

    // CHECK: %[[BROADCAST0:.*]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>}> : (tensor<4x1xf32>) -> tensor<4x4x1xf32>
    // CHECK: %[[RESHAPE0:.*]] = mhlo.reshape %[[BROADCAST0]] : (tensor<4x4x1xf32>) -> tensor<4x4xf32>
    // CHECK: %[[UPDATED_ARG0:.*]] = "mhlo.broadcast_in_dim"(%[[RESHAPE0]]) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<4x4xf32>) -> tensor<4x4x4xf32>

func.func @div_dense_different_rank() -> tensor<1x2x2xf32> {
  %cst_0 = arith.constant dense<[[[1.0],[2.0]]]> : tensor<1x2x1xf32>
  %cst_1 = arith.constant dense<[[2.0, 3.0]]> : tensor<1x2xf32>

  %0 = "tfl.div"(%cst_0, %cst_1) {fused_activation_function = "NONE"} : (tensor<1x2x1xf32>, tensor<1x2xf32>) -> tensor<1x2x2xf32>

  func.return %0 : tensor<1x2x2xf32>

func.func @testDilate(%arg0: tensor<3x4x5xf32>) -> tensor<5x7x9xf32> {
  // CHECK: "tfl.dilate"(%arg0, %cst, %cst_0)
  %cst = arith.constant dense<1> : tensor<3xi32>
  %cst_0 = arith.constant dense<-1.0> : tensor<f32>
  %0 = "tfl.dilate"(%arg0, %cst, %cst_0) : (tensor<3x4x5xf32>, tensor<3xi32>, tensor<f32>) -> tensor<5x7x9xf32>
  func.return %0 : tensor<5x7x9xf32>
  // CHECK: return %0 : tensor<5x7x9xf32>

func.func @foldFill() -> (tensor<3x2x1xf32>, tensor<*xf32>, tensor<*xcomplex<f32>>) {
  %0 = "tf.Const"() {value = dense<[3, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tf.Const"() {value = dense<23.0> : tensor<f32>} : () -> tensor<f32>
  // CHECK-DAG: "tf.Const"() <{value = dense<2.300000e+01> : tensor<3x2x1xf32>}>
  %2 = "tf.Fill"(%0, %1) : (tensor<3xi32>, tensor<f32>) -> tensor<3x2x1xf32>

    }) {device = "/device:CPU:0"} : () -> tensor<*xf32>
    // CHECK: "tf.Cast"(%{{.*}}) <{Truncate = false}> : (tensor<1x8x2xf32>) -> tensor<*xf32>
    // CHECK: (tensor<i32>, tensor<1x8x2xf32>) -> (tensor<1x8x1xf32>, tensor<1x8x1xf32>)
    %3:2 = "tf.Split"(%0, %1) {device = ""} : (tensor<i32>, tensor<*xf32>) -> (tensor<*xf32>, tensor<*xf32>)
    %4 = tensor.cast %1 : tensor<*xf32> to tensor<?x?x?xf32>