// func.func func_name_${key1}_fn (...) {
//   ...${key2}...
// }
// ```
// The above template with generate two functions by substituting `key1` and
// `key2` with given values.

module {

  for main_op in ["Conv2D", "DepthwiseConv2D", "MatMul"] {
    parameters[
      {"quantized_ops": ["${main_op}", "BiasAdd"], "act_func": "internal_requantize_no_activation_fn", "output_type": "!tf_type.qint8"},

// CHECK-SAME:      [[ARG:%.+]]: tensor<2x12x21x7xf16>
// CHECK:           [[CONV32:%.+]] = mhlo.convert %arg0 : (tensor<2x12x21x7xf16>) -> tensor<2x12x21x7xf32>
// CHECK:           [[ZERO:%.+]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK:           [[DIVIDEND:%.+]] = "mhlo.reduce_window"([[CONV32]], [[ZERO]])
// CHECK-SAME:        window_dimensions = dense<[1, 2, 2, 1]>

        Convention conv = new ExtensibleDynamicObject(this, DynamicObjectAwareBean.class, TestUtil.instantiatorFactory().decorateLenient()).getConvention();

        public Convention getConvention() {
            return conv;
        }

        public ExtensionContainer getExtensions() {
            return conv;
        }

    %1 = "quantfork.stats"(%arg0) {layerStats = dense<[1.27501142, 149.824783]> : tensor<2xf32>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>

  // Canonicalization includes const folding, which is utilized here to optimize
  // away ops that can't get constant folded after PrepareTF pass. For example,
  // tf.Conv2D is split into tf.Transpose and tfl.Conv2D.
  pass_manager->addNestedPass<mlir::func::FuncOp>(
      mlir::createCanonicalizerPass());
  pass_manager->addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());

// functions fail on a nil input.

// convT converts a value of type t, which is pointed to by v, to a pointer that can
// be used as the second word of an interface value.
func convT(t *_type, v unsafe.Pointer) unsafe.Pointer {
	if raceenabled {
		raceReadObjectPC(t, v, getcallerpc(), abi.FuncPCABIInternal(convT))
	}
	if msanenabled {
		msanread(v, t.Size_)
	}
	if asanenabled {

// CHECK: %[[DEQUANTIZE:.*]] = "tfl.dequantize"(%[[QUANTIZE]])
// CHECK: %[[CONV:.*]] = "tfl.conv_2d"(%arg0, %[[DEQUANTIZE]], %[[CONSTANT]])
// CHECK: return %[[CONV]]
}

// CHECK-LABEL: perChannelFakeQuantWithConv2D
func.func @perChannelFakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) {

  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x30x30x16xf32>
  func.return %0 : tensor<256x30x30x16xf32>
}

// -----

func.func @testConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x30x30x16xf32> {
  // expected-error @+1 {{requires positive strides}}

			{{if .Set}}
				<span class="cov0">not covered</span>
				<span class="cov8">covered</span>
			{{else}}
				<span class="cov0">no coverage</span>
				<span class="cov1">low coverage</span>
				<span class="cov2">*</span>
				<span class="cov3">*</span>
				<span class="cov4">*</span>
				<span class="cov5">*</span>
				<span class="cov6">*</span>
				<span class="cov7">*</span>
				<span class="cov8">*</span>

  %0 = "tf.Conv2D"(%output, %cst) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true}> {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", device...