.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

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();

} >&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=$classpath
<% if ( mainClassName.startsWith('--module ') ) { %>
MODULE_PATH=$modulePath

    // CreateConstOpWithSingleValue. Also, make sure it works with QConst.
    return failure();
  }

  auto zero_bias = rewriter.create<TFL::ConstOp>(op->getLoc(), bias_attr);
  op->setOperand(bias_idx, zero_bias);

  return success();
}

TF::ConstOp PadConstValues(Operation* input_op, int value_to_pad,
                           int pad_dimensions, Location loc,
                           OpBuilder* builder) {

        }
        operand = 0;
      }

      ShapedType type;
      float ratio_threshold = kBlockOverRandomSparsityRatio;
      if (isa<ConstOp>(inst)) {
        supported_block_size = sparse_op.GetFloatBlockSize();
        type = mlir::cast<ShapedType>(dyn_cast<ConstOp>(inst).getType());
      } else if (isa<QConstOp>(inst)) {
        supported_block_size = sparse_op.GetQuantizedBlockSize();

  }];

  let arguments = (ins
    StrAttr:$node_def,
    I32Attr:$op_key
  );

  let assemblyFormat = "attr-dict";
}

def ConstOp: TensorflowMlrt_Op<"constop", []> {
  let summary = "The tf_mlrt ConstOp";

  let description = [{
    The ConstOp creates a constant tensorflow::Tensor from serialized proto.
  }];

  let arguments = (ins
    StrAttr:$tensor_proto
  );

  let results = (outs