}

  func.func private @composite_conv2d_fn_1(%arg0: tensor<1x2x2x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {

      if (!input_type || !input_type.hasStaticShape()) {
        return false;
      }
      total_count += input_type.getNumElements();
    }
    *count = total_count;
    return true;
  }

  // For conv2d/depthwise_conv/fully_connected ops.
  // This algorithm actually comes from TOCO tooling_util.cc
  static bool GetArithmeticCountForConvAndFullyconnectedOp(mlir::Operation* op,

      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')

  %1 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {device = "", max = 2.000000e-01 : f32, min = -1.000000e-01 : f32, narrow_range = false, num_bits = 8 : i64} : (tensor<1x3x4x3xf32>) -> tensor<*xf32>
  %2 = "tf.Conv2D"(%1, %0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

  %dq_bias = "quantfork.dcast"(%q_bias) : (tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>) -> tensor<2xf32>

// 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 = ""}

    func.return %arg0 : tensor<*xi32>
  }

  // Test conv2d inferReturnTypes can infer some information when input or
  // filter does not have fully static shape.

  // CHECK-LABEL: func @conv2d_unranked_input_and_filter
  func.func @conv2d_unranked_input_and_filter(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: -> tensor<?x?x?x?xf32>

    %5 = "tf.Pad"(%arg0, %3) : (tensor<2x224x224x3xf32>, tensor<4x2xi32>) -> tensor<2x230x230x3xf32>
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: strides = [1, 1, 1, 1]
    // CHECK-SAME: (tensor<2x115x115x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32>

	return *(*uint32)(unsafe.Pointer(&f))
}

// Ensure OCONVNOP is zero cost.
func Conv(v uint64) uint64 { // ERROR "can inline Conv"
	return conv2(conv2(conv2(v))) // ERROR "inlining call to (conv1|conv2)"
}
func conv2(v uint64) uint64 { // ERROR "can inline conv2"
	return conv1(conv1(conv1(conv1(v)))) // ERROR "inlining call to conv1"
}
func conv1(v uint64) uint64 { // ERROR "can inline conv1"

	return *(*uint32)(unsafe.Pointer(&f))
}

// Ensure OCONVNOP is zero cost.
func Conv(v uint64) uint64 { // ERROR "can inline Conv"
	return conv2(conv2(conv2(v))) // ERROR "inlining call to (conv1|conv2)"
}
func conv2(v uint64) uint64 { // ERROR "can inline conv2"
	return conv1(conv1(conv1(conv1(v)))) // ERROR "inlining call to conv1"
}
func conv1(v uint64) uint64 { // ERROR "can inline conv1"