func.func @main(%arg0 : tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> {
  func.return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>
}

// CHECK:          func.func @main(%[[ARG0:.*]]: tensor<!tf_type.variant<tensor<2xi32>>>, %[[ARG1:.*]]: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>>

      %0  = "tf.Const"() {value = dense<[56, 904]> : tensor<2xi32>} : () -> tensor<2xi32>
      // CHECK: %{{.*}} = stablehlo.custom_call @tf.ResizeBilinear(%arg0, %{{.*}}) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<1x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>

  // expected-error@+1 {{'mhlo.uniform_dequantize' op is illegal as it is a UQ op or contains uq/qint types}}
  %0 = mhlo.uniform_dequantize %arg0 : (tensor<?x?x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<?x?xf32>
  return %0 : tensor<?x?xf32>
}

// -----

func.func @illegal_tf_uniform_quantize(%arg0 : tensor<1xf32>) -> tensor<1xf32> {

// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

func.func @main(%arg0: tensor<4xf32>, %arg1: tensor<4x!quant.uniform<u8:f32, 0.1>>) -> tensor<4xf32> {
  "tfl.NumericVerify"(%arg1, %arg0) {tolerance = 0.1 : f32} : (tensor<4x!quant.uniform<u8:f32, 0.1>>, tensor<4xf32>) -> (tensor<4xf32>)
  func.return %arg0 : tensor<4xf32>

// CHECK-NEXT: }

func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tfl.less"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1>
  %1 = "tf.If"(%0, %arg0, %arg1) {else_branch = @cond_false, then_branch = @cond_true, is_stateless = false} : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %1 : tensor<1xf32>
}

  %cst_0 = "tf.Const"() {value = dense<0.0235294122> : tensor<f32>} : () -> tensor<f32>
  %cst_1 = "tf.Const"() {value = dense<0.00708661414> : tensor<1xf32>} : () -> tensor<1xf32>
  %cst_2 = "tf.Const"() {value = dense<1.799000e+03> : tensor<1xf32>} : () -> tensor<1xf32>

  },  {
    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>, %arg4: tensor<i32>):
      %1 = "tfl.sub"(%arg2, %arg4) {fused_activation_function = "NONE"} :
        (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
      %2 = tfl.add %arg3, %arg3 {fused_activation_function = "NONE"} : tensor<*xf32>
      "tfl.yield"(%1, %2, %arg4) : (tensor<*xi32>, tensor<*xf32>, tensor<i32>) -> ()
  }) : (tensor<i32>, tensor<1xf32>, tensor<i32>) -> (tensor<i32>, tensor<1xf32>, tensor<i32>)

  %1 = "tfl.unidirectional_sequence_rnn"(%arg0, %arg1, %arg2, %arg3, %0) {fused_activation_function = "TANH", time_major = true} : (tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4x4xf32>) -> tensor<4xf32>
  func.return %1 : tensor<4xf32>

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [

static constexpr char kMlirModuleStr[] = R"(
  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
    %0 = "tf.Acos"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
   func.return %0 : tensor<1xf32>
  }
})";

static constexpr char kBadMlirModuleStr[] = R"(
  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {