%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} {

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

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>