%strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
  %lhs_dilation = "tf.Const"() {value = dense<[4, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>

func.func @tensorlistConst(%arg0 : tensor<1xi32>) -> tensor<2x3xi32> {
  // CHECK-DAG: %[[ELEMENT0:.*]] = "tf.Const"() <{value = dense<[0, 1, 2]> : tensor<3xi32>}> : () -> tensor<3xi32>
  // CHECK-DAG: %[[ELEMENT1:.*]] = "tf.Const"() <{value = dense<[3, 4, 5]> : tensor<3xi32>}> : () -> tensor<3xi32>
  // CHECK: %[[LIST:.*]] = "tf.Pack"(%[[ELEMENT0]], %[[ELEMENT1]]) <{axis = 0 : i64}> : (tensor<3xi32>, tensor<3xi32>) -> tensor<2x3xi32>

  %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-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>

  %0 = "tfr.cast"(%arg0) : (tensor<*xf32>) -> !tfr.tensor
  %1 = "tfr.cast"(%0) : (!tfr.tensor) -> tensor<*xi32>
  %2 = "tfr.cast"(%0) : (!tfr.tensor) -> tensor<2xi32>
  func.return %1, %2 : tensor<*xi32>, tensor<2xi32>

// CHECK: %[[tf_cast_unranked:.*]] = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<*xf32>) -> tensor<*xi32>

  %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>

// CHECK-NEXT: %[[s3:.*]] = "tfl.shape"(%arg1) : (tensor<?x?x4x!quant.uniform<u8:f32, 1.000000e+00>>) -> tensor<3xi32>
// CHECK-NEXT: %[[s2]], %[[s3]] : tensor<?xi32>, tensor<3xi32>
}

// CHECK-LABEL: QuantizeMultipleUsers
func.func @QuantizeMultipleUsers(%arg1: tensor<?x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1xi32>,tensor<1xi32>) {

  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 384, 32]> : tensor<3xi32>} : () -> tensor<3xi32>
  %2 = func.call @simple_test(%arg0, %0, %1) {tac.interface_name = "func1"} : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %2 : tensor<1x384x32xf32>
}

// PARTIAL-LABEL: @simple_test

  // CHECK-DAG: [[OFFSET_2:%.*]] = "tf.Const{{.*}} dense<[0, 5, 0]> : tensor<3xi32>

  %offset:3 = "tf.ConcatOffset"(%concat_dim, %shape0, %shape1, %shape2) : (tensor<i32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) -> (tensor<3xi32>, tensor<3xi32>, tensor<3xi32>)

  // CHECK: return [[OFFSET_0]], [[OFFSET_1]], [[OFFSET_2]]
  func.return %offset#0, %offset#1, %offset#2: tensor<3xi32>, tensor<3xi32>, tensor<3xi32>
}