} >&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();

                   .Input(FakeInput(DT_INT32))
                   .Attr("T", DT_INT32)
                   .Device("/job:localhost/replica:0/task:0/device:XLA_CPU:0")
                   .Finalize(node_def()));
  TF_EXPECT_OK(InitOp());

  // Add inputs.
  Tensor* a = CreateDeviceTensor<int32>(TensorShape({1, 3}), {1, 2, 3});
  Tensor* b = CreateDeviceTensor<int32>(TensorShape({1, 3}), {4, 5, 6});
  inputs_.push_back({nullptr, a});

  }];

  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

  }

  void runOnOperation() override;
};

// Returns True iff the otuput value of `op` is either a compile time constant
// or bounded from the XLA compiler's perspective, even if it is not a
// `ConstOp`.
bool IsOutputCompileTimeConstantOrBounded(Operation* op) {
  return llvm::isa_and_nonnull<TF::ShapeOp, TF::ShapeNOp, TF::RankOp,
                               TF::SizeOp, TF::TensorArraySizeV3Op,

                                    pad_beg, pad_end, 0,       0};
  auto paddings = DenseIntElementsAttr::get(padding_type, values);
  // Update pad_op paddings.
  op.setOperand(1, builder.create<TF::ConstOp>(loc, paddings));

  // Set input type.
  auto input = op.getOperand(0);
  SmallVector<int64_t, 4> transform_shape = {
      pad_input_shape[0], pad_input_shape[1] / block_size,

Value CreateScalarConst(int32_t value, OpBuilder builder, Location loc) {
  auto attr = DenseIntElementsAttr::get(
      tensorflow::GetTypeFromTFTensorShape({}, builder.getI32Type()), value);
  return builder.create<TF::ConstOp>(loc, attr);
}

Value GetR1Const(ArrayRef<int64_t> r1, OpBuilder builder, Location loc,
                 int bitwidth) {
  llvm::SmallVector<APInt, 4> values;
  int64_t rank = r1.size();
  values.reserve(rank);

}

Value BuildIntConstOp(ImplicitLocOpBuilder& builder,
                      ConversionPatternRewriter& rewriter, int64_t const_value,
                      Type type) {
  Value result_const =
      builder.create<TF::ConstOp>(rewriter.getIntegerAttr(type, const_value));
  return result_const;
}

Value BuildIntArrayConstOp(ImplicitLocOpBuilder& builder,
                           ConversionPatternRewriter& rewriter,

      if (i != feature_index) {
        reduce_dim_axes.push_back(i);
      }
    }
    auto mean_dims = DenseIntElementsAttr::get(dims_type, reduce_dim_axes);
    ::mlir::TF::ConstOp reduce_dim_op =
        rewriter.create<TF::ConstOp>(bn_op.getLoc(), mean_dims);
    int64_t feature_dim_size = input_type.getDimSize(feature_index);
    auto mean_var_type = RankedTensorType::get(/*shape=*/{feature_dim_size},