func.return %0#2, %0#3, %0#4 : tensor<i32>, tensor<256x256xf32>, tensor<?x256x256xf32>
}

func.func @while_body_11_frozen0(%arg0: tensor<*xi32>, %arg1: tensor<*xi32>, %arg2: tensor<*xi32>, %arg3: tensor<*xf32>, %arg4: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xi32>, tensor<*xi32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = arith.constant dense<[1, 0]> : tensor<2xi32>
  %cst_0 = arith.constant dense<0> : tensor<i32>

  %0 = "tf.Less"(%arg0, %arg4) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<*xi1>

    %9 = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
    %10 = "tf.Less"(%arg3, %9) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i1>
    "tfl.yield"(%10) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<!tf_type.resource>, %arg5: tensor<f32>):

  // CHECK: "mhlo.batch_norm_inference"({{.*}}, %arg1, %arg2, %arg3, %arg4) <{epsilon = 1.000000e-03 : f32, feature_index = 3 : i64}> : (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>) -> tensor<8x8x8x8xf32>

  ^bb0(%arg3: tensor<*xi32>, %arg4: tensor<1xf32>, %arg5: tensor<2xf32>):
    %1 = func.call @WhileOp_cond(%arg3, %arg4, %arg5) : (tensor<*xi32>, tensor<1xf32>, tensor<2xf32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg3: tensor<*xi32>, %arg4: tensor<2xf32>, %arg5: tensor<1xf32>):

  %0 = "tf.Const"() {_output_shapes = ["tfshape$"], device = "", dtype = f32, value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>

// CHECK:  %[[BATCHNORM1_a:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[ADD2]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  %[[BATCHNORM1_b:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[BATCHNORM1_a]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  "tf.FusedBatchNormV3"(%[[BATCHNORM1_b]], %[[ARG1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
}


func.func @batchNormWithGlobalNormalization(

        def componentId = DefaultModuleComponentIdentifier.newId(id)

        def artifacts = [new PublishArtifactLocalArtifactMetadata(componentId, new DefaultPublishArtifact("art1", "zip", "art", null, new Date(), new File("art1.zip")))]
        def defaultVariant = createConfigurationMetadata("default", componentId, [], artifacts)

const char* art = R"(
  ___  ___  __  __ _       ___                     _
 / _ \|   \|  \/  | |     / __|___ _ ___ _____ _ _| |_ ___ _ _
| (_) | |) | |\/| | |__  | (__/ _ \ ' \ V / -_) '_|  _/ -_) '_|
 \___/|___/|_|  |_|____|  \___\___/_||_\_/\___|_|  \__\___|_|
)";

int main(int argc, char* argv[]) {
  tensorflow::InitMlir y(&argc, &argv);
  llvm::errs() << art << "\n";

                      %arg2: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint8>>>,
                      %arg3: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint16>>>,
                      %arg4: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint32>>>) {
  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint8