%one = "tf.Const"() {value = dense<1> : tensor<4xi32>} : () -> tensor<4xi32>
    %0 = "tf.StridedSlice"(%input, %zero, %one, %one) {begin_mask = 15 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<1024x1024x1024x1024xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) -> tensor<*xf32>

    // CHECK: tensor<1x1x1x1xf32>
    func.return %0 : tensor<*xf32>

    %0 = tf_executor.graph {
      %out, %ctl = tf_executor.island wraps "tf.Const"() {device = "", value = dense<-1> : tensor<i64>} : () -> tensor<i64>
      %out_0, %ctl_1 = tf_executor.island wraps "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "1", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

  %6 = tfr.quant_raw_data(%5) : (!tfr.tensor_list) -> !tfr.tensor_list
  %7 = "tfr.cast"(%cst_1) : (tensor<i64>) -> !tfr.tensor
  %8 = tfr.call @tf__concat(%7, %6) : (!tfr.tensor, !tfr.tensor_list) -> !tfr.tensor
  func.return %8 : !tfr.tensor

// CHECK: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<1> : tensor<i64>}> : () -> tensor<i64>
// CHECK: %[[BUILD_LIST_0:.*]] = "tfr.build_list"(%arg1, %arg0) : (!tfr.tensor, !tfr.tensor) -> !tfr.tensor_list

    %0:3 = "tf.Unpack"(%arg0) {axis = 0 : i64} : (tensor<3xi64>) -> (tensor<i64>, tensor<i64>, tensor<i64>)
    return
  }
})";

    %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }
// Checks that the entry function is quantized for convolution. Quantized

    return %0 : tensor<1x3xf32>
    // CHECK: %[[CALL:.+]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME: _entry_function = @composite_dot_general_fn_1

      // expected-remark@above {{ID: 0}}
      %1 = "tf.RandomUniform"(%0) {device = "", seed = 3 : i64, seed2 = 5 : i64} : (tensor<1xi32>) -> tensor<3xf32>
      // expected-remark@above {{ID: 1}}
      // expected-remark@above {{Successors: {4}}}
      %2 = "tf.RandomUniform"(%0) {device = "", seed = 3 : i64, seed2 = 5 : i64} : (tensor<1xi32>) -> tensor<3xf32>
      // expected-remark@above {{ID: 2}}

          "tf.Yield"(%6) : (tensor<i64>) -> ()
        }, {
          "tf.Yield"(%arg3) : (tensor<i64>) -> ()
        }) {_lower_using_switch_merge = true, is_stateless = false} : (tensor<i1>) -> tensor<i64>
        "tf.Yield"(%3, %4) : (tensor<i1>, tensor<i64>) -> ()

       (tensor<i1>, tensor<!tf_type.resource<tensor<f32>>>, tensor<f32>) -> tensor<f32>
  %5 = "tf.Identity"(%4) : (tensor<f32>) -> tensor<f32>
  %6 = "tf.Pack"(%2, %5) {N = 2 : i64, T = f32, axis = 0 : i64, device = ""} : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
  func.return %6 : tensor<2xf32>
}

// -----

// Tests resource passed in as an argument is not modified and not returned.

  %2 = "tf.XlaCallModule"(%1#0, %0) <{Sout = [#tf_type.shape<1x3>], dim_args_spec = [], disabled_checks = [], has_token_input_output = false, module = "", platforms = [], version = 5 : i64}> {_entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1", _quantization_method = "static_range_ptq {}", _stablehlo_module_attrs = {}, _tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<1x1024xf32>,...