%0 = "tfr.cast"(%arg0) : (tensor<1x3x!quant.uniform<i8:f32:1, {0.1:1, 0.2:2, 0.3:3}>>) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%0) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %1 = "tfr.cast"(%scale) : (!tfr.tensor) -> tensor<3xf32>
  %2 = "tfr.cast"(%zp) : (!tfr.tensor) -> tensor<3xi32>
  func.return %1, %2 : tensor<3xf32>, tensor<3xi32>

  %b = arith.constant dense<0.0> : tensor<16xf32>
  %conv_3d = "tfl.conv_3d"(%arg0, %w, %b) {dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x32x32x32x8xf32>, tensor<1x1x1x8x16xf32>, tensor<16xf32>) -> tensor<1x32x32x32x16xf32>
  func.return %conv_3d : tensor<1x32x32x32x16xf32>

  %1 = "tf.TPUCompileMlirAndExecute"(%0) {metadata = "metadata", mlir_module = "mlir_module", operandSegmentSizes = array<i32: 1, 0>, producer_name = "producer_name"} : (tensor<f32>) -> tensor<i32>
  func.return %1 : tensor<i32>
}

// -----

func.func @executeop_input_same_execute_op(%arg0: tensor<i32>, %arg1: tensor<2xf32>) -> (tensor<i32>) {

// CHECK: {
// CHECK-NEXT:   version: 3,
// CHECK-NEXT:   operator_codes: [ {

// CHECK: {
// CHECK-NEXT:   version: 3,
// CHECK-NEXT:   operator_codes: [ {

  attributes {tf_saved_model.exported_names = ["assign_from_global"]} {
    // CHECK: [[name:%.*]] = "tf.VarHandleOp"
    // CHECK: "tf.AssignVariableOp"([[name]]
    %0 = "tf.Const"() {type = tensor<10xf32>, value = dense<[0.,10.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32>} : () -> tensor<10xf32>
    "tf.AssignVariableOp"(%arg0, %0) : (tensor<!tf_type.resource<tensor<10xf32>>>, tensor<10xf32>) -> ()

    %cst_0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>

    %var_0 = "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
    "tf.AssignVariableOp"(%var_0, %cst_0) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()

    %var_1 = "tf.VarHandleOp"() {shared_name = "var_1"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>

  %cst_1 = "tf.Const"() {value = dense<[0.3, 0.4, 0.3, 0.4, 0.3, 0.4, 0.3, 0.4]> : tensor<8xf32>} : () -> tensor<8xf32>

  } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
  %4 = "tf.BiasAdd"(%3, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>

  func.return %2, %4 : tensor<*xf32>, tensor<*xf32>

// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<2xf32>}> : () -> tensor<2xf32>

      %outputs_45, %control_46 = tf_executor.island wraps "tf.Const"() {device = "/device:CPU:0", value = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
      %outputs_47, %control_48 = tf_executor.island wraps "tf.Const"() {device = "/device:CPU:0", value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>