// Create a tfl.transpose op that performs ZX transpose on `input`.
  auto create_z_x_transpose_op = [&](Value input) -> Value {
    RankedTensorType input_type = mlir::cast<RankedTensorType>(input.getType());
    const int input_rank = input_type.getRank();

    // Create a 1D I32 tensor for representing the dimension permutation.
    auto permuation_tensor_type =

                shape=input_shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
        options=save_options,
    )
    return model

  def _create_gather_model(self, input_type, use_variable) -> module.Module:
    class GatherModel(module.Module):
      """A simple model with a single gather."""

      def __init__(self, use_variable):
        """Initializes a GatherModel.

// of `input_type`, if the `quant_dim` is valid. On the other hand, the
// symmetry of min and max is not adjusted by this method. The QAT workflow
// should set min/max correctly (and use `narrow_range`=true, `is_signed`=true)
// if symmetric quantization is required.
TypeAttr GetQuantizedTypeAttr(Builder builder, Type input_type, Attribute min,

std::unique_ptr<OperationPass<ModuleOp>> CreateOptimizeFunctionalOpsPass();

std::unique_ptr<OperationPass<func::FuncOp>> CreateModifyIONodesPass(
    mlir::Type input_type, mlir::Type output_type);

std::unique_ptr<OperationPass<func::FuncOp>> CreateModifyIONodesPass();

// Creates an instance of the TensorFlow Lite dialect PostQuantizeRemoveQDQ
// pass.

      ('use_constant_with_int64_input', np.int64, False),
      ('use_variable_with_int64_input', np.int64, True),
  )
  @test_util.run_v2_only
  def test_gather_model(self, input_type, use_variable):
    model = self._create_gather_model(input_type, use_variable)

    save.save(model, self._input_saved_model_path)

    rng = np.random.default_rng(seed=42)
    static_input_shape = [6]

    // If no padding is negative return the input as is.
    if (llvm::all_of(explicit_padding, [](int64_t pad) { return pad >= 0; })) {
      return value;
    }

    auto input_type = mlir::cast<RankedTensorType>(value.getType());
    auto input_shape = input_type.getShape();

    llvm::SmallVector<int64_t, 4> start;
    llvm::SmallVector<int64_t, 4> size;
    start.reserve(explicit_padding.size() / 2);

                                   *index);
  }
  return absl::OkStatus();
}

// Returns the data type of the destination of an edge.
DataType EdgeType(const Edge* edge) {
  return edge->dst()->input_type(edge->dst_input());
}

// Adds the control inputs of `node` to `*deps`.
void AddControlInputs(const Node& node, absl::flat_hash_set<Node*>* deps) {
  for (const Edge* edge : node.in_edges()) {

	1, // 1: istio.networking.nds.v1.NameTable.TableEntry.value:type_name -> istio.networking.nds.v1.NameTable.NameInfo
	2, // [2:2] is the sub-list for method output_type
	2, // [2:2] is the sub-list for method input_type
	2, // [2:2] is the sub-list for extension type_name
	2, // [2:2] is the sub-list for extension extendee
	0, // [0:2] is the sub-list for field type_name
}

func init() { file_dns_proto_nds_proto_init() }

                        const bool enable_per_channel_quantized_weight) {
  const GemmStyleOp gemm_style_op =
      *entry_func_op.getOps<GemmStyleOp>().begin();

  const Type input_type = entry_func_op.getArgumentTypes()[0];
  const Type filter_type = entry_func_op.getArgumentTypes()[1];
  const Type func_result_type = entry_func_op.getResultTypes()[0];

  if (inserted) {
    NodeDef arg_def;
    NodeDefBuilder builder(
        absl::StrCat(src_node->name(), "_", src_slot, "_arg"), kArgOp,
        NodeDebugInfo(src_node->def()));
    DataType dtype = edge->dst()->input_type(edge->dst_input());
    builder.Attr("T", dtype);
    builder.Attr("index", arg_index);
    Status s = builder.Finalize(&arg_def);
    if (!s.ok()) return s;