}

func.func @identity(%arg0: tensor<10xi32>, %arg1: tensor<20xi32>, %arg2: tensor<30xi32>) -> (tensor<10xi32>, tensor<20xi32>, tensor<30xi32>, tensor<*xi32>) {
  %0 = "tf.Identity"(%arg0) : (tensor<10xi32>) -> tensor<10xi32>
  %1:2 = "tf.IdentityN"(%arg1,%arg2) : (tensor<20xi32>, tensor<30xi32>) -> (tensor<20xi32>, tensor<30xi32>)
  %2 = "tf.Identity"(%arg0) : (tensor<10xi32>) -> tensor<*xi32>

  // CHECK: return [[SELECT]]
  %0 = "tf.FloorDiv"(%arg0, %arg1) : (tensor<2x3xi32>, tensor<3xi32>) -> tensor<2x3xi32>
  func.return %0: tensor<2x3xi32>
}

// -----

// CHECK-LABEL: func @floordiv_reverse_broadcast_i32
func.func @floordiv_reverse_broadcast_i32(%arg0: tensor<3xi32>, %arg1: tensor<2x3xi32>) -> tensor<2x3xi32> {

    %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>

}

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
}

  %0 = "tfl.pseudo_const"() { value = dense<[1, 32, 32, 16]> : tensor<4xi32> } : () -> tensor<4xi32>
  %1 = "tfl.pseudo_qconst"() {qtype = tensor<16x!quant.uniform<i32:f32, 1.0>>, value = dense<1> : tensor<16xi32>} : () -> tensor<16x1x1x8x!quant.uniform<i32:f32, 1.0>>
  %2 = "tfl.pseudo_qconst"() {qtype = tensor<16x!quant.uniform<i32:f32, 1.0>>, value = dense<2> : tensor<16xi32>} : () -> tensor<16x!quant.uniform<i32:f32, 1.0>>

  func.return %2 : tensor<1x128x32xf32>

// CHECK-DAG:  [[VAL_1:%.*]] = arith.constant dense<0> : tensor<3xi32>
// CHECK-DAG:  [[VAL_2:%.*]] = arith.constant dense<[1, 128, 32]> : tensor<3xi32>
// CHECK:  [[VAL_3:%.*]] = "tfl.slice"(%arg0, [[VAL_1]], [[VAL_2]]) : (tensor<4x128x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x128x32xf32>
}

// -----

// CHECK-LABEL: @WhileCanonicalizeBug

// 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>) {

// CHECK-NEXT:      %10 = "tfl.concatenation"(%9, %7, %8) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<3xi32>
// CHECK-NEXT:      %11 = mhlo.dynamic_reshape %arg0, %10 : (tensor<2x2x?x3xf32>, tensor<3xi32>) -> tensor<2x?x3xf32>
// CHECK-NEXT:      %12 = "tfl.shape"(%arg1) : (tensor<2x4x?x3xf32>) -> tensor<4xi32>

  func.return %0#0 : tensor<2xi32>
}

// -----

func.func @unpack(%arg0: tensor<2x3xi32>) -> tensor<3xi32> {
  // CHECK: "tfl.unpack"(%arg0) <{axis = -2 : i32, num = 2 : i32}>
  %0:2 = "tfl.unpack"(%arg0) {axis = -2 : i32, num = 2 : i32} : (tensor<2x3xi32>) -> (tensor<3xi32>, tensor<3xi32>)
  func.return %0#0 : tensor<3xi32>
}

  %cst_perm = arith.constant dense<0> : tensor<1xi32>

  // CHECK: %[[CST:.*]] = arith.constant dense<{{\[}}1, 2, 3]> : tensor<3xi32>
  // CHECK: return %[[CST]]
  %0 = "tfl.transpose"(%cst, %cst_perm) : (tensor<3xi32>, tensor<1xi32>) -> tensor<3xi32>
  func.return %0 : tensor<3xi32>
}

// CHECK-LABEL: @transpose_dynamic
func.func @transpose_dynamic() -> tensor<?xi32> {
  %cst = arith.constant dense<[1, 2, 3]> : tensor<3xi32>