%b = arith.constant dense<0.0> : tensor<512xf32>
  %fc = "tfl.fully_connected"(%arg0, %w, %b) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<512x12xf32>, tensor<512xf32>) -> tensor<1x112x112x512xf32>
  func.return %fc : tensor<1x112x112x512xf32>

// CHECK: %[[b:.*]] = arith.constant dense<0.000000e+00> : tensor<512xf32>

  %1 = "tfl.pseudo_const"() { value = dense<1.0> : tensor<16xf32>} : () -> tensor<16xf32>
  %2 = "tfl.conv_3d"(%arg0, %0, %1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, dilation_d_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32, stride_d = 1 : i32} : (tensor<1x32x32x32x8xf32>, tensor<1x1x1x8x16x!quant.uniform<i8:f32, 1.0>>, tensor<16xf32>) -> tensor<1x32x32x32x16xf32>

    return %1 : tensor<1x3xf32>
  }

  func.func private @composite_matmul_with_bias_fn_2(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>, %arg2: tensor<3xf32>) -> tensor<1x3xf32> attributes {tf_quant.composite_function} {

  // Just to introduce an extra use for %cst.
  %1 = "tf.AddV2"(%cst, %cst) {device = ""} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>

  return %0 : tensor<16xf32>
}
// Check that the constant is cloned with same value.
// CHECK-DAG: %[[CST_0:.*]] = "tf.Const"()
// CHECK-SAME: value = dense<1> : tensor<1xi32>
// CHECK-DAG: %[[CST_1:.*]] = "tf.Const"()

  %0 = "tfl.unidirectional_sequence_lstm"(%arg0,...

    %0 = "tf.Sub"(%cst_0, %cst_1) : (tensor<8xf32>, tensor<8xf32>) -> tensor<8xf32>
    return %0 : tensor<8xf32>
  }
// CHECK: @serving_default
// CHECK-DAG: %[[VAR_HANDLE_2:.*]] = "tf.VarHandleOp"() {{.*shared_name = "const_0".*}} : () -> tensor<!tf_type.resource<tensor<8xf32>>>
// CHECK-DAG: %[[READ_VAR_0:.*]] = "tf.ReadVariableOp"(%[[VAR_HANDLE_2]]) : (tensor<!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>

  %2 = chlo.broadcast_add %1, %bias : (
      tensor<?x2x2x1xi32>, tensor<1xi32>) ->tensor<?x2x2x1xi32>
  return %2 : tensor<?x2x2x1xi32>
}

// -----

// CHECK-LABEL: func @convolution_add_add_static
func.func @convolution_add_add_static(
    %lhs: tensor<2x3x2x1xi8>, %rhs: tensor<2x1x1x1xi8>,
    %zp_offset: tensor<2x2x2x1xi32>, %bias: tensor<1xi32>
  ) -> tensor<2x2x2x1xi32> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution

func.func @squeeze_folder(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32> {
  %0 = "tfl.squeeze"(%arg0) : (tensor<?x?xf32>) -> tensor<?x?xf32>
  // CHECK: return %arg0
  func.return %0 : tensor<?x?xf32>
}

// -----

// CHECK-LABEL: @squeeze_folder
func.func @squeeze_folder(%arg0 : tensor<?x?xf32>) -> tensor<*xf32> {
  %0 = "tfl.squeeze"(%arg0) : (tensor<?x?xf32>) -> tensor<*xf32>
  // CHECK: "tfl.squeeze"

func.func @tf2xla_fallback_op_approx_top_k(%arg0: tensor<16xf32>) -> (tensor<?xf32>, tensor<?xi32>) {
  %0:2 = "tf_device.cluster"() ({
    // CHECK: tf.ApproxTopK
    // CHECK-NOT: _xla_outside_compilation
    %1:2 = "tf.ApproxTopK"(%arg0) {k = 2} : (tensor<16xf32>) -> (tensor<?xf32>, tensor<?xi32>)
    tf_device.return %1#0, %1#1 : tensor<?xf32>, tensor<?xi32>