(CreateTFShapeOp $input, $input, ConstBoolAttrTrue))),
          [(TensorOf<[TF_Int, TF_Float, TF_Complex]> $updates)]>;

//===----------------------------------------------------------------------===//
// Selu op patterns.
//===----------------------------------------------------------------------===//

def getScale : NativeCodeCall<
  "GetScalarOfType(getElementTypeOrSelf($0), 1.0507009873554804934193349852946)"
  >;

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


  %6 = "tf.Conv2D"(%arg0, %arg1) {
    data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],

  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,
  LSH_PROJECTION = 15,
  LSTM = 16,
  MAX_POOL_2D = 17,
  MUL = 18,
  RELU = 19,
  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,

  return %2 : tensor<*xf32>
}

// CHECK-LABEL: gelu_aten
// CHECK: %0 = "tfl.gelu"(%arg0) <{approximate = false}> : (tensor<5x10xf32>) -> tensor<5x10xf32>

func.func private @gelu_decomp_2(%arg0: tensor<5x10xf32>) -> tensor<5x10xf32>
func.func @gelu_aten_approximate(%arg0: tensor<5x10xf32>) -> (tensor<*xf32>) {

                        (TFL_RangeOp $start, $limit, $delta)>;
def LegalizeRelu6 : Pat<(TF_Relu6Op $arg), (TFL_Relu6Op $arg)>;
def LegalizeRelu : Pat<(TF_ReluOp $arg), (TFL_ReluOp $arg)>;
// TFL Relu doesn't support I32/I64 type, so legalizes TF Relu to TFL Maximum.
def LegalizeReluI32 :
  Pat<(TF_ReluOp TensorOf<[I32]>:$arg),
      (TFL_MaximumOp $arg,
        (Arith_ConstantOp ConstantAttr<RankedI32ElementsAttr<[]>,"0">))>;

      padding: str = 'SAME',
      has_func_alias: bool = False,
  ) -> module.Module:
    class ConvModel(module.Module):
      """A simple model with a single conv2d, bias and relu."""

      def __init__(self):
        self.out_channel_size = filter_shape[-1]

        # This ensures filters will have different value range per out channel
        self.filters = np.stack(
            [

    llvm::StringRef str, flatbuffers::FlatBufferBuilder* builder) {
  return llvm::StringSwitch<tflite::ActivationFunctionType>(str)
      .Case("NONE", tflite::ActivationFunctionType_NONE)
      .Case("RELU", tflite::ActivationFunctionType_RELU)
      .Case("RELU_N1_TO_1", tflite::ActivationFunctionType_RELU_N1_TO_1)
      .Case("RELU6", tflite::ActivationFunctionType_RELU6)
      .Case("TANH", tflite::ActivationFunctionType_TANH)