// set of acceptable options.
// LINT.IfChange
enum BuiltinOperator : int32 {
  ADD = 0,
  AVERAGE_POOL_2D = 1,
  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,

      if (fc_op.getFilter() != filter) {
        // This filter goes through quantize and dequantize ops. Then we just
        // need to update the weight to the quantize op.
        filter.replaceAllUsesWith(new_filter_op);
      } else {
        // This filter doesn't go through quantize and dequantize ops, Then
        // we update the weight of the affine op directly.

  %2 = "tfl.dequantize"(%1) : (tensor<1x384x384x!quant.uniform<i8:f32, 0.003:-128>>) -> tensor<1x384x384xf32>
  %3 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<1x1x384xf32>} : () -> tensor<1x384x384xf32>

// set of acceptable options.
// LINT.IfChange
enum BuiltinOperator : int32 {
  ADD = 0,
  AVERAGE_POOL_2D = 1,
  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,

    ([CVE-2022-21728](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21728))
*   Fixes a heap OOB access in `Dequantize`
    ([CVE-2022-21726](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21726))
*   Fixes an integer overflow in shape inference for `Dequantize`
    ([CVE-2022-21727](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21727))
*   Fixes a heap OOB access in `FractionalAvgPoolGrad`

// Base struct for quantization.
template <QuantizationTrait quantization_trait, typename ConcreteT,
          typename RootOpT = DequantizeOp>
struct TFLQuantizationBase
    : public quant::QuantizationPattern<ConcreteT, QuantizeOp, DequantizeOp,
                                        NumericVerifyOp, RootOpT> {
  explicit TFLQuantizationBase(MLIRContext* ctx,

  // The original model reshape->custom->custom->squeeze.
  ASSERT_THAT(*float_graph->operators(), SizeIs(4));
  // The resulting model should be:
  // reshape->dequantize->custom->custom->quantize->squeeze.
  ASSERT_THAT(subgraph->operators, SizeIs(6));
  const std::vector<BuiltinOperator> op_codes = {
      BuiltinOperator_RESHAPE,  BuiltinOperator_DEQUANTIZE,

  // before converting TF_Conv to TFL_Conv
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));

  // Remove the wrapper of the tf.FakeQuant* ops and also insert the
  // tfl.quantize and tfl.dequantize to preserve the quantization parameters.
  // This is done after the first round of optimization to make sure all the
  // min/max operands of the tf.FakeQuant* are constants to be matched. The

foreach BinaryOp = [TFL_DivOp, TFL_MulOp]<Op> in
  defm : FuseMulOrDivWithConv2dOrDepthwiseConv2d<BinaryOp>;


// This pattern applies when the same quantize/dequantize have been used twice
// with the same scale. We want to remove the redundancy.
// TODO(fengliuai): move this to the sanity check of pre-quantize pass.
def eliminate_dq_q_pairs : Pat<
  (TFL_QuantizeOp (TFL_DequantizeOp $in), $qt),
  (replaceWithValue $in),

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s

// Tests for PopulateFusedGemmStylePatterns are handled in
// quantize_composite_functions for module-level evaluation of functions.

module attributes {tf_saved_model.semantics} {
// CHECK: quantize_simple_xla_call_module(%[[ARG_0:.+]]: tensor<1x4xf32>)
  func.func private @quantize_simple_xla_call_module(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {