}) {sym_name = "__inference_wrapped_function_4260250_0", sym_visibility = "private", tf._input_shapes = [#tf_type.shape<>], tf.signature.is_stateful, function_type = (tensor<i32>) -> ()} : () -> ()
  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    "func.return"(%arg0) : (tensor<i32>) -> ()

                             %arg4: tensor<10xf32> {tf._user_specified_name = "b2"}) -> tensor<10xf32>
          attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<32x28x28x1>, #tf_type.shape<3x3x1x5>, #tf_type.shape<5>, #tf_type.shape<3920x10>, #tf_type.shape<10>]} {
  return %arg4 : tensor<10xf32>
}

// -----

// CHECK-LABEL: strips_attributes_with_tf_values

func.func private @batched_function(%arg0: tensor<1x3xf32> {tf._user_specified_name = "0"}, %arg1: tensor<*x!tf_type.resource>) -> tensor<1x3xf32> attributes {tf._input_shapes = [#tf_type.shape<1x3>, #tf_type.shape<*>], tf.signature.is_stateful} {
  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> tensor<1x3xf32>

    }
    func.return %0 : tensor<*xf32>
  }
  func.func private @__inference_call_440(%arg0: tensor<?x28x28x3xf32> {tf._user_specified_name = "inputs"}) -> tensor<*xf32> attributes {tf._input_shapes = [#tf_type.shape<?x28x28x3>], tf.signature.is_stateful} {
    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<32xf32>} : () -> tensor<32xf32>

  %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> {device = ""} : () -> tensor<i32>

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

class ShapeInference
    : public impl::TensorFlowShapeInferencePassBase<ShapeInference> {
 public:
  ShapeInference() = default;
  explicit ShapeInference(ArrayRef<ArrayRef<int64_t>> input_shapes)
      : input_shapes_(input_shapes) {}
  void runOnOperation() override {
    // Parse `input_arg_shapes_` if provided (test only)
    SmallVector<ArrayRef<int64_t>> input_shapes_vec;

                  .Finalize(&def));
  shape_inference::InferenceContext c(0, def, op_def, {S({3, 4})}, {}, {}, {});
  std::vector<shape_inference::ShapeHandle> input_shapes;
  TF_CHECK_OK(c.input("input", &input_shapes));
  ASSERT_EQ("[3,4]", c.DebugString(input_shapes[0]));
  TF_CHECK_OK(reg->shape_inference_fn(&c));
  ASSERT_EQ("[3,4,8]", c.DebugString(c.output(0)));
}

TEST(BitcastOpTest, TestShapeInference_SmallerShape) {

  // data = { input_shape, axes_ones }
  //      = { [2, 3, 5, 7], [1, 1] }
  // The input_rank_range entry in indices first replicates the
  // input_shape to the result.
  // The axes entry in indices then moves a 1 to each of its entries,
  // resulting in
  // [2, 1, 1, 7]
  std::vector<Output> indices = {input_rank_range, axes};
  std::vector<Output> data = {input_shape, axes_ones};
  return DynamicStitch(scope, indices, data);
}

GraphdefToMlirTranslateFunction(
    llvm::StringRef input, const std::vector<std::string>& input_arrays,
    const std::vector<std::string>& input_dtypes,
    const std::vector<std::optional<std::vector<int>>>& input_shapes,
    const std::vector<std::string>& output_arrays,
    const std::vector<std::string>& control_output_arrays,
    const GraphdefToMlirOptions& import_options, mlir::MLIRContext* context);

ABSL_DEPRECATED(