%conv2 = "tfl.conv_2d"(%4, %5, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x112x112x32xf32>, tensor<32x3x3x3xf32>, tensor<32xf32>) -> tensor<1x56x56x32xf32>

// CHECK:  %1 = "tfl.conv_2d"(%arg0, %0, %[[CONSTANT]]) <{dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x8x7x16xf32>
// CHECK:  %2 = "tf.Conv2D"

// CHECK:           %[[VAL_5:.*]] = "tfl.conv_2d"(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x30x30x16xf32>

    // Only rank size four input will be only available by the tf.Conv2D
    // operator verification.
    if (!input_type || input_type.isDynamicDim(3)) {
      return failure();
    }
    // Check if the given op is based on grouped convolution.
    // Dim size zero will be verified by the tf.Conv2D operator verification.
    if (input_type.getDimSize(3) % filter_type.getDimSize(2) != 0) {

  const auto& subgraph = model_.subgraphs[0];
  auto conv_op = subgraph->operators[0].get();
  const int input_tensor_idx = 0;
  const int weights_tensor_idx = 1;
  const int bias_tensor_index = 2;
  const int output_tensor_idx = 0;
  const auto bias_tensor =
      subgraph->tensors[conv_op->inputs[bias_tensor_index]].get();
  const auto input_tensor =
      subgraph->tensors[conv_op->inputs[input_tensor_idx]].get();

      def body(self, x, w):
        z = nn_ops.conv2d(x, w, padding='SAME')
        return z, w

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(
                  shape=input_shape, dtype=dtypes.float32, name='input_tensor'
              )
          ]
      )
      def main(self, x):
        x1 = nn_ops.conv2d(x, self.w, padding='SAME')

// TF-DAG: "tf.DumpTensor"(%[[conv0_float]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv_with_dump", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_2", node_name = "Conv2D"}> {device = ""}

    %2 = "tf.Cast"(%identity) {Truncate = false} : (tensor<2x3x3x2xi8>) -> tensor<2x3x3x2xi32>
    %3 = "tf.Sub"(%2, %arg6) : (tensor<2x3x3x2xi32>, tensor<i32>) -> tensor<2x3x3x2xi32>
    %4 = "tf.Conv2D"(%1, %3) {dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xi32>, tensor<2x3x3x2xi32>) -> tensor<1x3x2x2xi32>

        ),
    )
    quantization.quantize_saved_model(
        self._input_saved_model_path,
        self._output_saved_model_path,
        config,
    )

    expected_outputs = model.conv2d(input_data)

    root = load.load(self._output_saved_model_path)
    self.assertCountEqual(root.signatures.keys(), {'serving_default'})

    new_outputs = root.signatures['serving_default'](

type SockaddrL2TPIP struct {
	Addr   [4]byte
	ConnId uint32
	raw    RawSockaddrL2TPIP
}

func (sa *SockaddrL2TPIP) sockaddr() (unsafe.Pointer, _Socklen, error) {
	sa.raw.Family = AF_INET
	sa.raw.Conn_id = sa.ConnId
	sa.raw.Addr = sa.Addr
	return unsafe.Pointer(&sa.raw), SizeofSockaddrL2TPIP, nil
}

// SockaddrL2TPIP6 implements the Sockaddr interface for IPPROTO_L2TP/AF_INET6 sockets.
type SockaddrL2TPIP6 struct {