%1 = "tf.BiasAdd"(%0, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<1xf32>) -> tensor<*xf32>
  %2 = "tf.Relu6"(%1) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>
  func.return %2 : tensor<*xf32>
}

// CHECK-LABEL: func @conv_with_dynamic_channel_dim

// CHECK: while_1([[ARG0:%[^ :]*]]: tensor<i32>, [[ARG1:%[^ :]*]]: tensor<1xf32>)
func.func @while_1(%arg0: tensor<i32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  // CHECK: [[CST:%[^ ]*]] = arith.constant dense<1> : tensor<i32>
  // CHECK: "tfl.while"([[ARG0]], [[ARG1]])
  // CHECK: (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)
  %0:2 = "tfl.while"(%arg0, %arg1) (
    // cond
    {

    %1 = "tfl.abs"(%arg2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32>
    %2 = stablehlo.add %1, %arg2 : tensor<5xf32>
    %3 = "tfl.abs"(%2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32>
    "stablehlo.return"(%3) : (tensor<5xf32>) -> ()
  }) {dimensions = array<i64: 0>} : (tensor<7x5xf32>, tensor<5xf32>) -> tensor<5xf32>
  func.return %0: tensor<5xf32>
}

// -----

// 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>
}

  ^bb1(%0: tensor<!tf_type.resource<tensor<?xf32>>>, %1: tensor<!tf_type.resource<tensor<?xf32>>>, %2: tensor<!tf_type.resource<tensor<?xf32>>>):
    "tf.AssignVariableOp"(%0, %arg0) : (tensor<!tf_type.resource<tensor<?xf32>>>, tensor<?xf32>) -> ()
    cf.br ^bb1(%1, %2, %0 : tensor<!tf_type.resource<tensor<?xf32>>>, tensor<!tf_type.resource<tensor<?xf32>>>, tensor<!tf_type.resource<tensor<?xf32>>>)
  }

  %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>

  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}} {
    func.func @main() -> tensor<1xi32> {

  func.return %0#1 : tensor<1xf32>
}

func.func @cond(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> tensor<i1> {
  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

func.func @body(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>) {

// CHECK:     "tfl.yield"(%[[RES1]]) : (tensor<*xf32>) -> ()
// CHECK:   }) : (tensor<1xf32>) -> tensor<*xf32>
// CHECK:   return %0 : tensor<*xf32>
// CHECK: }
func.func @main(%arg0: tensor<1xf32>) -> tensor<*xf32> {
  %0 = "tf.While"(%arg0) {cond = @cond, body = @body, is_stateless = false} : (tensor<1xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

  // 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> {