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) {

} >&2

# OS specific support (must be 'true' or 'false').
cygwin=false
msys=false
darwin=false
nonstop=false
case "$( uname )" in                #(
  CYGWIN* )         cygwin=true  ;; #(
  Darwin* )         darwin=true  ;; #(
  MSYS* | MINGW* )  msys=true    ;; #(
  NONSTOP* )        nonstop=true ;;
esac

CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar

  func.walk([&](Operation* op) {
    // TODO(renjieliu): Find a generic way to deal with const ops.
    if (op->hasTrait<OpTrait::IsTerminator>() ||
        llvm::isa<TFL::QConstOp, TFL::ConstOp>(op) ||
        llvm::isa<TFL::QConstOp, TFL::ConstOp, TF::ConstOp, ConstOp>(op))
      return;

    bool int8_type_observed = false;
    bool uint8_type_observed = false;
    for (auto& input : op->getOpOperands()) {

      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());

// Returns a TF Constant tensor with the passed in values.
TF::ConstOp GetI64ConstantTensor(PatternRewriter &rewriter,
                                 ArrayRef<int64_t> values, Location location) {
  auto cst_attr = rewriter.getI64TensorAttr(values);
  return rewriter.create<TF::ConstOp>(location, cst_attr.getType(), cst_attr);
}

// Rewrites broadcast->reshape to a reshape->broadcast that reduces

      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));

} >&2

# OS specific support (must be 'true' or 'false').
cygwin=false
msys=false
darwin=false
nonstop=false
case "$( uname )" in                #(
  CYGWIN* )         cygwin=true  ;; #(
  Darwin* )         darwin=true  ;; #(
  MSYS* | MINGW* )  msys=true    ;; #(
  NONSTOP* )        nonstop=true ;;
esac

CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar