continue;
    }

    if (node->type_string() == "Const") {
      // Skip Const op with type DT_STRING, since XLA autoclustering doesn't
      // support it.
      const AttrValue* attr = node->attrs().Find("dtype");
      if (attr != nullptr && attr->type() == DT_STRING) {
        continue;
      }
    }

    if (!allowlist.empty() && !allowlist.contains(node->def().op())) {

  const std::string& func_str =
      "    signature {"
      "      name: 'FunctionWith__OP_NAME__'"
      "      output_arg {"
      "        name: 'out'"
      "        type: DT_STRING"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'error_op'"
      "      op: '__OP_NAME__'"
      "    }"
      "    ret {"
      "      key: 'out'"

*   Fixes an integer truncation vulnerability in code using the work sharder
    API,
    ([CVE-2020-15202](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15202))
*   Fixes a format string vulnerability in `tf.strings.as_string`,
    ([CVE-2020-15203](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15203))
*   Fixes segfault raised by calling session-only ops in eager mode,

    return std::nullopt;
  }

  // TensorFlow and TensorFlow Lite use different string encoding formats.
  // Convert to TensorFlow Lite format is it's a constant string tensor.
  if (tensor.dtype() == tensorflow::DT_STRING) {
    ::mlir::TFL::MiniDynamicBuffer dynamic_buffer;
    auto flat = tensor.flat<::tensorflow::tstring>();
    for (int i = 0; i < flat.size(); ++i) {
      const auto& str = flat(i);

  inputs.reserve(assets.size());
  for (const auto& asset : assets) {
    TensorInfo tensor_info;
    tensor_info.set_name(asset.tensor_name);
    tensor_info.set_dtype(DT_STRING);
    tensor_info.mutable_tensor_shape();
    inputs.push_back({asset.tensor_name, tensor_info});
  }

  std::unordered_map<std::string, std::string> tf_name_to_mlir_name;

For Unicode, see the
[https://www.tensorflow.org/tutorials/representation/unicode](Working with Unicode text)
tutorial.

Examples:

>>> tf.strings.as_string([3, 2])
<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'3', b'2'], dtype=object)>
>>> tf.strings.as_string([3.1415926, 2.71828], precision=2).numpy()
array([b'3.14', b'2.72'], dtype=object)
  }];

  let arguments = (ins

		{"UnsupportedType.Tag", Field, 13},
		{"VoidType", Type, 0},
		{"VoidType.CommonType", Field, 0},
	},
	"debug/elf": {
		{"(*File).Close", Method, 0},
		{"(*File).DWARF", Method, 0},
		{"(*File).DynString", Method, 1},
		{"(*File).DynValue", Method, 21},
		{"(*File).DynamicSymbols", Method, 4},
		{"(*File).ImportedLibraries", Method, 0},
		{"(*File).ImportedSymbols", Method, 0},
		{"(*File).Section", Method, 0},

pkg debug/elf, const STV_HIDDEN = 2
pkg debug/elf, const STV_INTERNAL = 1
pkg debug/elf, const STV_PROTECTED = 3
pkg debug/elf, const Sym32Size = 16
pkg debug/elf, const Sym64Size = 24
pkg debug/elf, method (*File) DynString(DynTag) ([]string, error)
pkg debug/elf, type FileHeader struct, Entry uint64
pkg debug/macho, const Cpu386 = 7
pkg debug/macho, const CpuAmd64 = 16777223
pkg debug/macho, const LoadCmdDylib = 12