std::vector<TF_Filesystem_Option_Value_Union> option_values(values.size());
  for (size_t i = 0; i < values.size(); i++) {
    memset(&option_values[i], 0, sizeof(option_values[i]));
    option_values[i].real_val = values[i];
  }
  option_value.values = &option_values[0];
  option.value = &option_value;
  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);

// - graphdef and MLIR tracing engine
INSTANTIATE_TEST_SUITE_P(Tracing, UnifiedCAPI,
                         ::testing::Combine(::testing::Values("graphdef",
                                                              "mlir"),
                                            ::testing::Values(false)));

}  // namespace

      return entry->second;
    } else {
      // Remove the stale block and continue.
      RemoveFile_Locked(key.first);
    }
  }

  // Insert a new empty block, setting the bookkeeping to sentinel values
  // in order to update them as appropriate.
  auto new_entry = std::make_shared<Block>();
  lru_list_.push_front(key);
  lra_list_.push_front(key);
  new_entry->lru_iterator = lru_list_.begin();

  return t;
}

TF_Tensor* Int32Tensor(const std::vector<int32_t>& values) {
  int64_t dims = values.size();
  return Int32Tensor(&dims, 1, values.data());
}

TF_Tensor* Int32Tensor(int32_t v) {
  const int num_bytes = sizeof(int32_t);
  int32_t* values = new int32_t[1];
  values[0] = v;
  return TF_NewTensor(TF_INT32, nullptr, 0, values, num_bytes,
                      &Int32Deallocator, nullptr);

  return op_->SetAttrTypeList(attr_name, values, num_values);
}
Status SetAttrBoolList(AbstractOperation* op_, const char* attr_name,
                       const unsigned char* values, int num_values,
                       ForwardOperation* forward_op_) {
  std::unique_ptr<bool[]> b(new bool[num_values]);
  for (int i = 0; i < num_values; ++i) {
    b[i] = values[i];
  }

// identify output values when possible and does other aggressive strategies.
// This may cause incorrectness in graph analyses, but is useful for simulation
// or scheduling.
// If include_input_tensor_values is true, the values of constant
// tensors will included in the input properties.
// If include_output_tensor_values is true, the values of constant tensors will
// be included in the output properties.

    if (index >= num_values) break;
    values[index] = p;
    lengths[index] = s.size();
    if ((p + s.size()) > (static_cast<char*>(storage) + storage_size)) {
      tsl::Set_TF_Status_from_Status(
          status,
          absl::InvalidArgumentError(
              "Not enough storage to hold the requested list of nodes"));
      return;
    }
    memcpy(values[index], s.data(), s.size());

                         int num_values) override {
    op_->node_builder.Attr(attr_name,
                           ArraySlice<const DataType>(values, num_values));
    return absl::OkStatus();
  }
  Status SetAttrBoolList(const char* attr_name, const unsigned char* values,
                         int num_values) override {
    std::unique_ptr<bool[]> b(new bool[num_values]);
    for (int i = 0; i < num_values; ++i) {

                                                 int num_values);
TF_CAPI_EXPORT extern void TFE_OpSetAttrTypeList(TFE_Op* op,
                                                 const char* attr_name,
                                                 const TF_DataType* values,
                                                 int num_values);
TF_CAPI_EXPORT extern void TFE_OpSetAttrShapeList(

                            const float* values, int num_values) {
  auto s =
      tensorflow::unwrap(op)->SetAttrFloatList(attr_name, values, num_values);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name,
                          const int64_t* values, int num_values) {
  auto s =