func.func @not_lift_float_conv(%arg0: tensor<1x3x4x512xf32>) -> (tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<3.000000e+00> : tensor<2x3x512x512xf32>} : () -> tensor<2x3x512x512xf32>
  %0 = "tf.Conv2D"(%arg0, %cst) {
    data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],
    padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true
  } : (tensor<1x3x4x512xf32>, tensor<2x3x512x512xf32>) -> tensor<*xf32>

  %cst2 = arith.constant dense<[1.0, 2.0]> : tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %cst0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<1x112x112x3xf32>, tensor<1x3x3x2xf32>) -> tensor<1x28x23x2xf32>

// CHECK:  [[VAL_3:%.*]] = "tfl.reshape"([[VAL_2]], %{{.*}}) : (tensor<1x1x1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>

// RUN: tf-quant-opt %s -split-input-file -quant-propagate-quantize-type | FileCheck %s

module {
  func.func @not_propagate_matmul(%arg0: tensor<1x2x2x2xf32>) -> tensor<*xf32> {
    %cst = "tf.Const"() {value = dense<127> : tensor<2x1024xi8>} : () -> tensor<2x1024xi8>
    %cst_0 = "tf.Const"() {value = dense<0.0157480314> : tensor<f32>} : () -> tensor<f32>
    %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>

  func.return %0 : tensor<?x32x32x16xf32>
}

func.func @testDepthwiseConv2D(tensor<1x112x112x3xf32>, tensor<1x3x3x32xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32> {
^bb0(%arg0: tensor<1x112x112x3xf32>, %arg1: tensor<1x3x3x32xf32>, %arg2: tensor<32xf32>):
  // CHECK: _arithmetic_count = 7626752 : i64

  func.return %0 : tensor<256x30x30x16xf32>
}

// -----

func.func @testDepthwiseConv(%arg0: tensor<1x112x112x32xf32>, %arg1: tensor<1x3x3x32xf32>, %arg2: tensor<32xf32>) -> tensor<1x112x112x32xf32> {
  // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"