// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail

// _output_shapes and f.* attributes are removed during tf-to-tfrt lowering.
// CHECK-LABEL: func @remove_unused_attr
func.func @remove_unused_attr() {
  // CHECK: %out_op_chain = tfrt_fallback_async.executeop.seq(%arg0) key(0) cost({{.*}}) device("/device:CPU:0") "tf.SomeOp2"()

  %0 = "tf.ReadVariableOp"(%handle) {_output_shapes = ["tfshape$dim { size: 3 }"], device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<3xf32>>>) -> tensor<3xf32>

    %arg1: tensor<1x3xf32> {tf_saved_model.index_path = [0]})
      -> (tensor<3x3xf32> {tf_saved_model.index_path = []}) {
  // CHECK: {{%.*}} = corert.get_op_handler %arg0 "/device:GPU:0"
  %2 = "tf.MatMul"(%arg0, %arg1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:GPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  func.return %2 : tensor<3x3xf32>

// `ignore_unregistered_attrs` argument is set to true, the attributes which are
// not in the op registry will be ignored. If the `ignore_unregistered_attrs`
// argument is not set to true, _output_shapes attribute is added to nodes with
// ShapedType for the leading values with ShapedType in the results of the
// nodes. Set it to true if the returned NodeDef will be executed by the linked
// TF Eager runtime.

      end++;
    if (begin != end) {
      mlir::TF::ResultShapeRange output_shapes = {
          mlir::TF::ResultShapeIterator(begin),
          mlir::TF::ResultShapeIterator(end)};
      SetShapeAttribute("_output_shapes", output_shapes, attributes);
    }
  }

  return absl::OkStatus();
}

  %4 = "tf.Add"(%2, %arg2) : (tensor<?x8x10xf32>, tensor<8x10xf32>) -> tensor<?x8x10xf32>
  %5 = "tf.Add"(%arg1, %arg2) : (tensor<8x10xf32>, tensor<8x10xf32>) -> tensor<8x10xf32>
  %6 = "tf.Const"() {_output_shapes = ["tfshape$"], device = "/device:CPU:0", dtype = f32, value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>

// HLO-level inputs are supplied, and HLO-level outputs are produced.
// xla_params is the HLO-level inputs and returns is the HLO-level outputs.
// If unconditionally_use_output_shapes is true then the unregistered
// attribute _output_shapes is always used to set the output shapes of the ops.
ABSL_DEPRECATED(
    "Use v1/compile_tf_graph.h::CompileTensorflowGraphToHlo instead.")
Status BuildHloFromGraph(
    const Graph& graph, xla::XlaBuilder& builder,

  %cst_0 = arith.constant dense<1> : tensor<3xi32>
  %0 = "tf.Squeeze"(%arg0) {T = f32, _output_shapes = ["tfshape$dim { size: 4 } dim { size: 64 } dim { size: 64 }"], device = "", squeeze_dims = []} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>

                    )
                ]
            )
        )
        self.assertTrue(
            self._contains_op(
                output_graphdef,
                'Const',
                '_output_shapes',
                per_channel_size_attr,
            )
        )
    elif target_opset == quant_opts_pb2.UNIFORM_QUANTIZED:
      self.assertTrue(

  if (node.IsWhileNode()) {
    auto* output_shapes = node.attrs().Find("output_shapes");
    auto* element_types = node.attrs().Find("T");
    if (output_shapes && !output_shapes->list().shape().empty()) {
      const auto& output_shape = output_shapes->list().shape(idx);
      const auto& element_type = element_types->list().type(idx);
      return ConvertToMlirTensorType(output_shape, element_type, &builder);
    }
  }