dilation_width_factor = rewriter.getI32IntegerAttr(1);
  }

  StringAttr padding;
  if (!TFPaddingIsSameOrValid(op, &padding)) return failure();

  // TensorFlow Conv3D has no bias, optimization patterns will fuse Conv3D
  // with other ops can fill the bias.
  Value none = rewriter.create<TFL::NoValueOp>(
      op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());

  %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...

// CHECK:           %[[VAL_9:.*]] = "tf.Transpose"(%[[VAL_7]], %[[VAL_8]]) : (tensor<1x256x256x1xbf16>, tensor<4xi64>) -> tensor<1x1x256x256xbf16>

        ),
    )
    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'](

    if ((quantization_method_ == tensorflow::quantization::QuantizationMethod::
                                     METHOD_DYNAMIC_RANGE_INT8) &&
        (function_name.contains("batch_matmul") ||
         function_name.contains("conv3d"))) {
      call_op->removeAttr(kQuantTraitAttrName);
    }

    // TODO(b/270906404): Support weight-only gather for uniform quantized opset
    // in PTQ mode
    if (target_opset_ == OpSet::UNIFORM_QUANTIZED &&

			rows.raw = rows.raw[:0]
			test(tt.name, tt.in, tt.want)
		}
	})

	// The numbers below are only valid for 64-bit interface word sizes,
	// and gc. With 32-bit words there are more convT2E allocs, and
	// with gccgo, only pointers currently go in interface data.
	// So only care on amd64 gc for now.
	measureAllocs := false
	switch runtime.GOARCH {
	case "amd64", "arm64":

// 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]>

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-lift-quantizable-spots-as-functions | FileCheck %s

// CHECK-LABEL: @conv_fn(
// CHECK-SAME:          %[[ARG_0:.*]]: tensor<1x3x3x4xf32>
func.func @conv_fn(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>

  };
  return values;
}

inline const char * const *EnumNamesBuiltinOperator() {
  static const char * const names[208] = {
    "ADD",
    "AVERAGE_POOL_2D",
    "CONCATENATION",
    "CONV_2D",
    "DEPTHWISE_CONV_2D",
    "DEPTH_TO_SPACE",
    "DEQUANTIZE",
    "EMBEDDING_LOOKUP",
    "FLOOR",
    "FULLY_CONNECTED",
    "HASHTABLE_LOOKUP",
    "L2_NORMALIZATION",

*   Fixes a `CHECK` fail in `tf.sparse.cross` ([CVE-2022-35997](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35997))
*   Fixes a floating point exception in `Conv2D` ([CVE-2022-35996](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35996))
*   Fixes a `CHECK` fail in `AudioSummaryV2` ([CVE-2022-35995](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35995))