bool restrict_functionalization_to_compiled_nodes = false;
  // If true, enables shape inference on input.
  // TODO(jpienaar): This will be removed shortly.
  bool enable_shape_inference = true;
  // _output_shapes is an unregistered attribute which is used during
  // GraphConstructor::ConvertGraph to override shapes. It is unfortunately
  // not always set correctly (which is undesirable and should be addressed)

  // Create a bad output shape attr.
  AttrValue shape_attr;
  TensorShapeProto* shape_proto = shape_attr.mutable_list()->add_shape();
  shape_proto->add_dim()->set_size(1);
  builder.Attr("_output_shapes", shape_attr);

  TF_RETURN_IF_ERROR(builder.Finalize(&node));

  return CreateSingleOpGraph(node, {}, {DataType::DT_INT32});
}

absl::Status BuildHloFromGraph(Graph& graph, bool use_output_shapes) {

func.func private @gives_a_name_to_anonymous_iterators() {
  // CHECK: "tf.Iterator"
  // CHECK-SAME: output_shapes{{.*}}200x28x28x1{{.*}}200x10
  // CHECK-SAME: output_types = [f32, f32]
  // CHECK-SAME: shared_name = "_iterator1"
  %0 = "tf.AnonymousIteratorV3"() {output_shapes = [
    #tf_type.shape<200x28x28x1>,
    #tf_type.shape<200x10>], output_types = [f32, f32]} : () -> tensor<!tf_type.resource>

    %3 = "tf.RangeDataset"(%0, %1, %2) {device = "/device:CPU:0", output_shapes = [#tf_type.shape<>], output_types = [i64], metadata = ""} : (tensor<i64>, tensor<i64>, tensor<i64>) -> tensor<!tf_type.variant>
    // CHECK: tfrt_fallback_async.executeop key({{[0-9]+}}) cost({{.*}}) device("/device:CPU:0") "tf.FlatMapDataset"({{.*}}) {Targuments = [], metadata = "", output_shapes = [#corert.shape<>], output_types = [i64]} {f = "funcB"} : 1

    %3 = "tf.RangeDataset"(%0, %1, %2) {device = "/device:CPU:0", output_shapes = [#tf_type.shape<>], output_types = [i64], metadata = ""} : (tensor<i64>, tensor<i64>, tensor<i64>) -> tensor<!tf_type.variant>

  %3 = "tf.TensorSliceDataset"(%2) {device = "", output_shapes = [#tf_type.shape<>], metadata = ""} : (tensor<3xi32>) -> tensor<*x!tf_type.variant>
  // CHECK: "tf.MapAndBatchDataset"(%[[TSLICE]], %[[BSIZE:.*]], %[[NPC]]
  // CHECK-SAME: f = @"__inference_Dataset_map_<lambda>_80",
  %4 = "tf.MapDataset"(%3) {device = "",
           f = @"__inference_Dataset_map_<lambda>_80",
           output_shapes = [#tf_type.shape<>], output_types = [i32],

    ) {
    // CHECK: tf.ReduceDataset
    %1 = "tf.ReduceDataset"(%arg0, %arg1) {
      Targuments = [],
      Tstate = [i64], device = "",
      f = @__reduce_func_0, f._tf_data_function = true,
      output_shapes = [#tf_type.shape<>],
      output_types = [i64], use_inter_op_parallelism = true } : (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>)
    func.return
}

  std::array<int64_t, 4> output_shape;
  // NHWC <- NCHW
  output_shape[0] = composite_result_shape[0];
  output_shape[1] = composite_result_shape[2];
  output_shape[2] = composite_result_shape[3];
  output_shape[3] = composite_result_shape[1];

  auto input_type = mlir::cast<ShapedType>(old_op->getOperand(0).getType());

  return RankedTensorType::get(output_shape, input_type.getElementType());
}

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),
    $quant_input,
    $bias, $padding, $stride_h, $stride_w, $faf),
  (TFL_TransposeConvOp $output_shape, $quant_weights,
    $quant_input, $bias, $padding, $stride_h, $stride_w, $faf), 

  } : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>

  func.return %0 : tensor<!tf_type.variant>
// CHECK: "tf.MapDataset"(
// CHECK-SAME: <{f = @{{.*}}, metadata = "", output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string], preserve_cardinality = true, use_inter_op_parallelism = true}> {Targuments = []}
}

// CHECK-LABEL: TfTakeWhileDataset