auto reshape_sizes = DenseIntElementsAttr::get(reshape_type, new_shape);
  auto reshape_value =
      builder->create<TF::ConstOp>(input.getLoc(), reshape_sizes);
  return builder->create<TF::ReshapeOp>(input.getLoc(), reshape_result_type,
                                        input, reshape_value);
}

// Creates transpose op for shape to depth transform.
TF::TransposeOp GetTransposeOpForConv2DFilter(OpBuilder* builder, Value input) {

      } else {
        if (IsCompatibleTypeWithTFLCastOp(value.getType()) &&
            IsCompatibleTypeWithTFLCastOp(type)) {
          auto cast = b.create<CastOp>(yield_op->getLoc(), type, value);
          args.push_back(cast);
        } else {
          auto cast = b.create<TF::CastOp>(yield_op->getLoc(), type, value);
          args.push_back(cast);
        }
      }
    }
    args.append(new_args.begin(), new_args.end());

}

// Creates a wrapper island enclosing the `sub_op` dependent on
// `control_inputs`.
IslandOp CreateIsland(Operation* sub_op, ValueRange control_inputs,
                      OpBuilder builder) {
  assert(sub_op);
  auto control_type = ControlType::get(builder.getContext());
  auto island = builder.create<IslandOp>(

    // Calculate conjugate of X
    std::string name = "Conj_Log1p_Grad_X";
    TF_RETURN_IF_ERROR(SafeConj(ctx, X, &temp_output, name.c_str()));

    AbstractTensorHandlePtr Conj_X(temp_output);

    // Creates Ones
    name = "OnesLike_Log1p_Grad_X";
    TF_RETURN_IF_ERROR(OnesLike(ctx, Conj_X.get(), &temp_output, name.c_str()));

    AbstractTensorHandlePtr Ones_X(temp_output);

    name = "Add_Log1p_Grad_X";

      "_xla", "_oc", "cluster", name_attrs, "cluster_rewritten", "host_graph",
      host_compute_core, &fld, &shape_inference_graphs,
      &has_outside_compilation));

  // Check host graph is not created.
  EXPECT_EQ(fld.Find("host_graph"), nullptr);
}

TEST_F(ExtractOutsideCompilationForFunctionTest, OutsideCompilationInIf) {
  // Build the XLA computation func.
  // "const0" (bool)
  // "const1" (int32)

        auto s_qconst =
            builder.create<SparseQConstOp>(op->getLoc(), cst.getQtypeAttr(),
                                           cst.getValue(), s_param, new_value);
        value.replaceAllUsesWith(s_qconst.getResult());
        cst.erase();
      }

      if (result.needs_densify) {
        auto value = op->getOperand(operand);
        auto densify =
            builder.create<DensifyOp>(op->getLoc(), value.getType(), value);

      RankedTensorType::get(static_cast<int32_t>(1), builder.getI32Type());
  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(

           "Disable deadness related safety checks when clustering (this is "
           "unsound)."),
      Flag("tf_xla_disable_resource_variable_safety_checks_for_debugging",
           &mark_for_compilation_flags
                ->tf_xla_disable_resource_variable_safety_checks_for_debugging,
           "Disable resource variables related safety checks when clustering "
           "(this is unsound)."),

  explicit LiteSessionWrapper(std::unique_ptr<Session> wrapped)
      : wrapped_(std::move(wrapped)) {}

  Status Create(const GraphDef& graph) override {
    return absl::UnimplementedError("Session::Create()");
  }
  Status Create(GraphDef&& graph) override {
    return absl::UnimplementedError("Session::Create()");
  }

  Status Extend(const GraphDef& graph) override {
    return absl::UnimplementedError("Session::Extend()");

    // be categorized as cusom ops despite each of them may require different
    // behaviors. In that case, these ops can be marked in the custom map and
    // treated separately in this pass.

    auto custom_op = llvm::dyn_cast_or_null<CustomOp>(op);
    if (!custom_op) return false;

    // Custom op which is marked in the custom op map is quantizable.