Tensor t(first.tensor.dtype(), shape);

  // Collate the constituent Tensors.
  size_t offset = 0;
  for (auto const& e : v) {
    Tensor elem = e.tensor;
    if (first.tensor.dtype() == DT_STRING) {
      for (int i = 0; i < elem.NumElements(); ++i) {
        t.flat<tstring>()(offset + i) = elem.flat<tstring>()(i);
      }
      offset += elem.NumElements();
    } else {

	}
	defer f.Close()
	dynstrings, err := f.DynString(flag)
	if err != nil {
		t.Fatalf("DynString(%s) failed on %s: %v", flag, path, err)
	}
	return dynstrings
}

func AssertIsLinkedToRegexp(t *testing.T, path string, re *regexp.Regexp) {
	t.Helper()
	for _, dynstring := range dynStrings(t, path, elf.DT_NEEDED) {
		if re.MatchString(dynstring) {
			return
		}
	}

      TF_RETURN_IF_ERROR(ValidateFunctionNotRecursive(function));
    }
  }

  return absl::OkStatus();
}

Tensor CreateStringTensor(const string& value) {
  Tensor tensor(DT_STRING, TensorShape({}));
  tensor.scalar<tstring>()() = value;
  return tensor;
}

void AddAssetsTensorsToInputs(const StringPiece export_dir,

	"fmt"
	"strings"

	"github.com/tinylib/msgp/msgp"
	"github.com/zeebo/xxh3"
)

// Op is operation type.
//
//go:generate msgp -unexported -file=$GOFILE
//go:generate stringer -type=Op -output=msg_string.go -trimprefix=Op $GOFILE

// Op is operation type messages.
type Op uint8

// HandlerID is the ID for the handler of a specific type.
type HandlerID uint8

const (
	// OpConnect is a connect request.

      }
      op {
        name: "RestoreV2"
        input_arg {
          name: "prefix"
          type: DT_STRING
        }
        input_arg {
          name: "tensor_names"
          type: DT_STRING
        }
        input_arg {
          name: "shape_and_slices"
          type: DT_STRING
        }
        output_arg {
          name: "tensors"
          type_list_attr: "dtypes"
        }

  TF_RETURN_WITH_CONTEXT_IF_ERROR(
      bundle_reader->Lookup(kObjectGraphProtoKey, &object_graph_tensor),
      "SavedModel checkpoint does not contain object graph.");
  if (object_graph_tensor.dtype() != DT_STRING ||
      object_graph_tensor.dims() != 0 ||
      object_graph_tensor.NumElements() != 1) {
    return absl::Status(
        absl::StatusCode::kFailedPrecondition,

		if(/msie/.test(u) && parseInt(v, 10) == 7) {
			is_ie7 = true;
			css_string += '.jstree li a { border-width:0 !important; padding:0px 2px !important; } ';
		}
		// correct ff2 lack of display:inline-block
		if(!/compatible/.test(u) && /mozilla/.test(u) && parseFloat(v, 10) < 1.9) {
			is_ff2 = true;
			css_string += '' +
				'.jstree ins { display:-moz-inline-box; } ' +

  tsl::Set_TF_Status_from_Status(status, cc_status);
}

#endif  // LIBTPU_EXCLUDE_C_API_IMPL

namespace tensorflow {

void TensorInterface::Release() {
  if (Type() == DT_STRING && NumElements() > 0) {
    TF_TString* data = static_cast<TF_TString*>(Data());
    if (CanMove() && data != nullptr) {
      for (int64_t i = 0; i < NumElements(); ++i) {
        TF_TString_Dealloc(&data[i]);
      }

}

TEST(ConvertTypeToTensorTypeTest, ConvertStringTensor) {
  mlir::MLIRContext context;
  RegisterDialects(context);
  mlir::Builder b(&context);

  // Create the sample tensor to convert.
  Tensor tensor(DT_STRING, TensorShape({1, 2, 2, 1}));
  EXPECT_EQ(4, tensor.NumElements());
  auto Tt = tensor.flat<tstring>();
  Tt.setValues({"one", "two", "three", "four"});
  auto value_or_status = ConvertTensor(tensor, &b);

  if (node.type_string() == "Const") {
    const AttrValue* attr = node.attrs().Find("dtype");
    if (!op_filter_.allow_string_consts && attr != nullptr &&
        attr->type() == DT_STRING) {
      *uncompilable_reason =
          "Const op with type DT_STRING is not supported by XLA.";
      return false;
    }
  }

  // XLA does not offer guaranteed aliasing between the input and output of the