auto begin = rewriter.create<TF::ConstOp>(slice_op.getLoc(),
                                              slice_op.getStartIndices());
    auto end = rewriter.create<TF::ConstOp>(slice_op.getLoc(),
                                            slice_op.getLimitIndices());
    auto strides =
        rewriter.create<TF::ConstOp>(slice_op.getLoc(), slice_op.getStrides());

      auto mul_rhs = ExpandTo4DForConv(gamma_cst);
      broadcasted_gamma = rewriter.create<ConstOp>(loc, mul_rhs);
    } else if (isa<TFL::DepthwiseConv2DOp>(mul_op_lhs)) {
      auto mul_rhs = ExpandTo4DForDepthwiseConv(gamma_cst);
      broadcasted_gamma = rewriter.create<ConstOp>(loc, mul_rhs);
    } else {
      return failure();
    }

}

//===----------------------------------------------------------------------===//
// ConstOp
//===----------------------------------------------------------------------===//

void ConstOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
  setNameFn(getResult(), "cst");
}

OpFoldResult ConstOp::fold(FoldAdaptor adaptor) {
  assert(adaptor.getOperands().empty() && "constant has no operands");

// ConstOp
//===----------------------------------------------------------------------===//

OpFoldResult ConstOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  (void)operands;
  assert(operands.empty() && "constant has no operands");
  // Return the held attribute value.
  return getValue();
}

      slice_op.getShrinkAxisMask() != 1)
    return {};

  // Returns a value if the `value` is defined by a ConstOp with a single
  // integer element in it and has an expected rank.
  auto get_const_int = [](Value value,
                          int expected_rank) -> std::optional<int64_t> {
    auto const_op = dyn_cast_or_null<ConstOp>(value.getDefiningOp());
    if (!const_op) return std::nullopt;

  return Status(absl::StatusCode::kInvalidArgument, "Unsupported type");
}

static bool IsConst(Operation* op) {
  return isa<mlir::func::ConstantOp, mlir::arith::ConstantOp, mlir::TF::ConstOp,
             tfl::ConstOp, tfl::QConstOp, tfl::SparseConstOp,
             tfl::SparseQConstOp, mlir::TFL::NoValueOp,
             mlir::stablehlo::ConstantOp, mlir::vhlo::ConstantOpV1>(op);
}

  OpBuilder builder(&func.front().front());
  for (auto arg : func.getArguments()) {
    Value operand = op.getOperand(arg.getArgNumber());
    if (isa_and_nonnull<TF::ConstOp>(operand.getDefiningOp())) {
      xla_recv_at_host_op.getResult(arg.getArgNumber())
          .replaceAllUsesWith(
              builder.clone(*operand.getDefiningOp())->getResult(0));
    }
  }

		}
		err = manager.dsStore.Add(ds)
		if err != nil {
			t.Fatal(err)
		}

		expectSyncDaemonSets(t, manager, ds, podControl, 1, 0, 0)
	}
}

// DaemonSets should place onto NotReady nodes
func TestNotReadyNodeDaemonDoesLaunchPod(t *testing.T) {
	for _, strategy := range updateStrategies() {
		ds := newDaemonSet("foo")
		ds.Spec.UpdateStrategy = *strategy

        padded_output_tensor_shape, input_tensor_type.getElementType());

    // The pad values is provided as a const op.
    auto pad_value_const_op = rewriter.create<TFL::ConstOp>(
        loc, /*value=*/DenseIntElementsAttr::get(
            RankedTensorType::get({rank, 2}, rewriter.getIntegerType(32)),
            tfl_pad_values));

    return rewriter.create<TFL::PadOp>(

	if len(hostAliases) == 0 {
		return []byte{}
	}

	var buffer bytes.Buffer
	buffer.WriteString("\n")
	buffer.WriteString("# Entries added by HostAliases.\n")
	// for each IP, write all aliases onto single line in hosts file
	for _, hostAlias := range hostAliases {
		buffer.WriteString(fmt.Sprintf("%s\t%s\n", hostAlias.IP, strings.Join(hostAlias.Hostnames, "\t")))
	}
	return buffer.Bytes()
}