clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};
};

// Check if given indices in `val1` has same number of elements as given
// indices in `val2`.

                                            RewritePatternSet *patterns);

// Creates an instance of the ConvertTFQuantTypes pass, which will convert TF
// qint types to int types and surround TF UniformQuantized ops with qint <->
// int casts.
std::unique_ptr<OperationPass<func::FuncOp>> CreateConvertTFQuantTypesPass();

// Creates an instance of the VerifyQuantLegalization pass, which verifies all

      /*allow_mutable_tensors=*/true));

  // Generic MLIR optimization passes.
  pm.addPass(mlir::createCanonicalizerPass());
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());

  // Legalizes TF UniformQuantized types into MHLO.
  pm.addNestedPass<func::FuncOp>(
      mlir::quant::stablehlo::CreateConvertTFQuantOpsToMHLOPass());
  pm.addPass(mlir::createCanonicalizerPass());

  // TF -> StableHLO legalization.

          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};
};

bool QuantizePass::shouldKeepUnusedQdqPattern() {
  return target_opset_ == OpSet::XLA &&

  // Constructor used by the PassRegistration and enforce int8 quantization.
  // This is only used by test.
  explicit PrepareQuantizeDRQPass() : op_set_(OpSet::UNIFORM_QUANTIZED) {
    quant_specs_.inference_type = tensorflow::DT_QINT8;
  }

  // Constructor used by manually creating the pass.
  explicit PrepareQuantizeDRQPass(const QuantizationSpecs& quant_specs,

  // Generic MLIR optimization passes.
  pm.addPass(mlir::createCanonicalizerPass());
  pm.addPass(mlir::TF::CreateTFShapeInferencePass(input_arg_shapes));

  // Legalizes TF UniformQuantized types into MHLO. Part of the official
  // TF/XLA bridge component.
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::stablehlo::CreateConvertTFQuantOpsToMHLOPass());

  // Uses TF ops that mimic quantization behavior. Used when the corresponding
  // integer op is not yet present.
  TF = 1;
  // Uses TF XLA ops
  XLA = 2;
  // Uses TF Uniform Quantized ops
  UNIFORM_QUANTIZED = 3;
  // Uses the StableHLO Quantizer. StableHLO Quantizer will be available as
  // an option in the TF Quantizer in StableHLO Quantizer v1.
  STABLEHLO = 4;
}

    quantization_options.enable_per_channel_quantization = False

  if quantization_options.enable_per_channel_quantization and not (
      (
          quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED
          or quantization_options.quantization_method.preset_method
          == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
      )
      or (
          quantization_options.op_set

  }
  if (!v.getDefiningOp() || !llvm::isa<TF::CastOp>(v.getDefiningOp())) {
    return false;
  }
  return IsIntToQintCast(v.getDefiningOp());
}

bool IsTFUniformQuantizedOpLegal(Operation *op) {
  // UniformQuantized Ops are considered legal if its qint operands and
  // results are connected to TF CastOp.
  return op && llvm::all_of(op->getResults(), IsQintValueQintToIntCast) &&

      "ToBool",
      "TridiagonalSolve",
      "TridiagonalMatMul",
      "TruncatedNormal",
      "UniformDequantize",
      "UniformQuantize",
      "UniformQuantizedAdd",
      "UniformQuantizedClipByValue",
      "UniformQuantizedConvolution",
      "UniformQuantizedDot",
      "UniformRequantize",
      "Unique",
      "UniqueV2",
      "UpperBound",
      "UnsortedSegmentMax",