// ReadVariableOp patterns. The ConstOps are not erased. Returns the ConstOp ->
// shared_name mapping. The shared_name is the shared name of the corresponding
// VarHandleOp.
llvm::MapVector<TF::ConstOp, std::string> ReplaceConstOpUsesWithVariableReads(
    llvm::ArrayRef<TF::ConstOp> target_const_ops) {
  llvm::MapVector<TF::ConstOp, std::string> const_op_name_map{};

class QuantizeConstWeights : public OpRewritePattern<TF::ConstOp> {
 public:
  explicit QuantizeConstWeights(
      MLIRContext* context,
      const tensorflow::quantization::QuantizationOptions& quantization_options)
      : OpRewritePattern<TF::ConstOp>(context),
        quant_options_(quantization_options) {}

  LogicalResult matchAndRewrite(TF::ConstOp op,

	s.parseState = s.parseState[0:0]
	s.err = nil
	s.endTop = false
}

// eof tells the scanner that the end of input has been reached.
// It returns a scan status just as s.step does.
func (s *scanner) eof() int {
	if s.err != nil {
		return scanError
	}
	if s.endTop {
		return scanEnd
	}
	s.step(s, ' ')
	if s.endTop {
		return scanEnd
	}
	if s.err == nil {

                  .Input(inputs)
                  .Attr("ids", ids)
                  .Attr("calibration_methods", calibration_methods)
                  .Finalize(node_def()));
  ASSERT_THAT(InitOp(),
              StatusIs(tsl::error::INVALID_ARGUMENT,
                       HasSubstr("NodeDef missing attr 'output_file_path'")));
}

TEST_F(CalibrationStatisticsSaverTest, WrongNumInputs) {

      TransposeOp transpose = dyn_cast<TransposeOp>(user);
      if (!transpose) return;

      // With permutation defined by constant operation.
      ConstOp perm =
          dyn_cast_or_null<ConstOp>(transpose.getOperand(1).getDefiningOp());
      if (!perm) return;

      // With the same permutation indices.
      auto dense_elem_attr = mlir::dyn_cast<DenseElementsAttr>(perm.getValue());

      unique_func_name, original_point, result_type);
  return end_call_op;
}

QuantizedType CalculateUniformQuantParams(
    PatternRewriter& rewriter, TF::ConstOp op,
    tensorflow::quantization::QuantizationComponentSpec& weight_spec) {
  // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset
  // Currently, support symmetric, per-tensor, signed int8

      return failure();
    }

    TF::ConstOp scale_op;
    TF::ConstOp zp_op;

    // Reads quantization parameters from the quantized type, and converts
    // them to constants.
    rewriter.setInsertionPoint(qparams_op);
    Location loc = qparams_op->getLoc();
    if (auto qtype = cast_qtype.dyn_cast<quant::UniformQuantizedType>()) {
      scale_op = rewriter.create<TF::ConstOp>(

Value CreateZeroInput(Location loc, OpBuilder& builder, Attribute zero_attr,
                      DenseIntElementsAttr shape_attr) {
  ConstOp zero = builder.create<ConstOp>(loc, zero_attr);
  zero->setAttr(kICIWeightDistributionMlirBridgeMarker,
                builder.getBoolAttr(true));
  ConstOp shape = builder.create<ConstOp>(loc, shape_attr);
  shape->setAttr(kICIWeightDistributionMlirBridgeMarker,
                 builder.getBoolAttr(true));

  auto out_segids_cst = builder.create<TFL::ConstOp>(
      builder.getI32TensorAttr(flattened_out_segids));
  auto contracting_segids_cst = builder.create<TFL::ConstOp>(
      builder.getI32TensorAttr(flattened_contracting_segids));
  auto num_segids_tensor =
      builder.create<TFL::ConstOp>(DenseIntElementsAttr::get(
          RankedTensorType::get({}, builder.getIntegerType(32)), 1));

    }
    rewriter.replaceOp(op, new_results);
    return success();
  }
};

class TFConstOpQuantToIntPattern : public OpConversionPattern<TF::ConstOp> {
 public:
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(
      TF::ConstOp op, TF::ConstOpAdaptor adaptor,
      ConversionPatternRewriter &rewriter) const override {
    if (!IsIllegalType(op.getOutput().getType())) return failure();