"legalized by this pass, then the IR won't be changed so that "
             "tensorlist ops can pass through (default false).">,
      Option<
        "default_to_single_batch_", "default-to-single-batch", "bool", "false",
        "When specified to true, if the tensorlist ops has unspecified batch "
        "size, this pass will assume that the batch size is one to proceed "

  OpBuilder builder(list);
  llvm::SmallVector<int64_t, 8> element_shape;
  // Infer TensorList element shape from the return type first, and then from
  // the const element shape operand. We first check the return type because
  // shape inference might have successfully inferred the element shape from
  // write operations on the TensorList.
  if (failed(GetElementShapeFromResultType(list.getType(), &element_shape))) {

  // If `emit_builtin_tflite_ops` is true, TF Lite legalization passes will be
  // added, which produces TF Lite ops.
  bool emit_builtin_tflite_ops = true;
  // If `lower_tensor_list_ops` is true, tensorlist ops will be lowered to basic
  // TF ops before legalization to TF Lite dialect.
  bool lower_tensor_list_ops = false;
  // The allowlist of functions that would be preserved after trimming.

        "//tensorflow/core/platform:status_matchers",
        "//tensorflow/core/platform:statusor",
    ],
)

tf_cc_test(
    name = "libtf_tensor_test",
    size = "medium",
    srcs = ["tests/tensor_test.cc"],
    tags = ["no_oss"],  # TODO(b/193268458): Need to disable TFRT.
    deps = [
        ":libtf",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c/eager:abstract_context",

  let summary =
    "Replicate TensorList init ops for correct shape assignments in shape inference";

  let description = [{
    If we pass same TensorList to a while op as multiple arguments or just use
    the same TensorList at multiple places and assign different
    TensorListSetItem to elements of TensorList, the shape inference is then

    llvm::cl::init(false));

// NOLINTNEXTLINE
opt<bool> legalize_custom_tensor_list_ops(
    "legalize-custom-tensor-list-ops",
    llvm::cl::desc("Convert \"tf.TensorList*\" ops to \"tfl.custom_op\""
                   "if they can all be supported."),
    llvm::cl::init(false));

// NOLINTNEXTLINE
opt<bool> serialize_stablehlo_ops(
    "serialize-stablehlo-ops",

_Replicate TensorList init ops for correct shape assignments in shape inference_

If we pass same TensorList to a while op as multiple arguments or just use
the same TensorList at multiple places and assign different
TensorListSetItem to elements of TensorList, the shape inference is then
unable to identify the Shape of these args and thus the input TensorList
shape is unidentifiable.

    llvm::cl::location(emit_custom_ops), llvm::cl::init(false));

// NOLINTNEXTLINE
static opt<bool, true> lower_tensor_list_ops_flag(
    "lower-tensor-list-ops",
    llvm::cl::desc("Lower the TensorList ops within the TFLite dialect"),
    llvm::cl::location(lower_tensor_list_ops), llvm::cl::init(false));

// NOLINTNEXTLINE
static opt<bool, true> strip_debug_info_flag(

// Creates a pass that adds control dependencies to keep the relative execution
// order of operations with side effects frozen.
std::unique_ptr<OperationPass<func::FuncOp>> CreatePinOpsWithSideEffectsPass();

// Legalize TensorList Ops iff all of them are supported.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeTensorListPass();

// Reduce the type precision of some tensor types if all values within that

                .getDimSize(1),
            3);

  auto it = fused_lstm_func_.getBody().back().rbegin();
  EXPECT_EQ(it->getName().getStringRef(),
            mlir::func::ReturnOp::getOperationName());
  it++;  // tensor_cast
  it++;  // lstm
  EXPECT_EQ(it->getName().getStringRef(),
            mlir::TFL::LSTMOp::getOperationName());
  EXPECT_EQ(it->getNumOperands(), 24);
  EXPECT_EQ(it->getNumResults(), 1);