}

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

  // Async compilation returns nullptr executable without an error.
  if (!executable && !pjrt_executable) {
    DCHECK(!must_compile_);
    Tensor compilation_key(cpu_allocator, DT_STRING, TensorShape({}));
    Tensor compilation_successful(cpu_allocator, DT_BOOL, TensorShape({}));
    compilation_successful.scalar<bool>()() = false;
    ctx->set_output(0, compilation_key);

      return DT_INT64;
    case toco::IODataType::UINT8:
      return DT_UINT8;
    case toco::IODataType::UINT64:
      return DT_UINT64;
    case toco::IODataType::STRING:
      return DT_STRING;
    case toco::IODataType::BOOL:
      return DT_BOOL;
    case toco::IODataType::COMPLEX64:
      return DT_COMPLEX64;
    case toco::IODataType::COMPLEX128:
      return DT_COMPLEX128;

  // Build a constant default key to specify that the unformatting should
  // transform the variables to the original format.
  builder.setInsertionPointAfter(while_op);
  tensorflow::Tensor default_key_tensor(tensorflow::DT_STRING, {3});
  default_key_tensor.vec<tensorflow::tstring>()(0) = kDefaultShardingValue;
  default_key_tensor.vec<tensorflow::tstring>()(1) = kDefaultShardingValue;

                                     data_type.value());
    const auto& resolved_dtype = data_types[data_type.index()];
    if (resolved_dtype == DT_INVALID || resolved_dtype == DT_STRING ||
        resolved_dtype == DT_RESOURCE || resolved_dtype == DT_VARIANT ||
        IsRefType(resolved_dtype))
      return errors::InvalidArgument("Unsupported dtype at index ",

  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }
    TF_Tensor* dst = TF_TensorFromTensor(src, &status);

    case DT_INT64:
      return PrintArray(num_elts, t.flat<int64_t>().data());
    case DT_BOOL:
      return PrintArray(num_elts, t.flat<bool>().data());
    case DT_STRING: {
      string ret;
      for (int64_t i = 0; i < num_elts; ++i) {
        if (i > 0) strings::StrAppend(&ret, " ");
        strings::StrAppend(&ret, absl::CEscape(t.flat<tstring>()(i)));
      }
      return ret;

    Node* a = ops::SourceOp(
        "Const", builder.opts()
                     .WithName("A")
                     .WithAttr("dtype", DT_STRING)
                     .WithAttr("value", Tensor(DT_STRING, TensorShape())));
    Node* b = ops::UnaryOp("EncodeBase64", a, builder.opts().WithName("B"));
    ops::BinaryOp("StringSplit", a, b, builder.opts().WithName("C"));

                 const GraphDefBuilder::Options& opts) {
  return KnownShapeBase(DT_FLOAT, shape, opts);
}

Node* KeyPlaceholderShape(const GraphDefBuilder::Options& opts) {
  return KnownShapeBase(DT_STRING, {2}, opts);
}

Node* KeyPlaceholder(const string& call_node,
                     const GraphDefBuilder::Options& opts) {
  if (opts.HaveError()) return nullptr;