// Copy Tensor data into an array.
  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data
  for (int i = 0; i < data_len; i++) {
    EXPECT_EQ(result_data[i], 0);
  }

  TF_DeleteTensor(result_tensor);
  TF_DeleteAbstractTensor(result);
  TF_DeleteOutputList(o);
  TF_DeleteExecutionContext(ctx);

                                size_t proto_len, TF_Status* status) {
  // shape.ParseFromArray takes an int as length, this function takes size_t,
  // make sure there is no information loss.
  if (proto_len > std::numeric_limits<int>::max()) {
    status->status = InvalidArgument(
        "proto_len (", proto_len,
        " bytes) is too large to be parsed by the protocol buffer library");
    return;

index e966c03..22ab790 100644
--- a/locale/weightwc.h
+++ b/locale/weightwc.h
@@ -79,19 +79,19 @@ findidx (const wint_t **cpp, size_t len)
 	    if (cp[cnt] != usrc[cnt])
 	      break;
 
-	  if (cnt < nhere - 1)
+	  if (cnt < nhere - 1 || cnt == len)
 	    {
 	      cp += 2 * nhere;
 	      continue;
 	    }
 
-	  if (cp[nhere - 1] > usrc[nhere -1])
+	  if (cp[nhere - 1] > usrc[nhere - 1])

  const std::vector<std::string>& nodes =
      reinterpret_cast<const tensorflow::grappler::GrapplerItem*>(item)->fetch;

  const int len = std::min(num_values, static_cast<int>(nodes.size()));
  char* p = static_cast<char*>(storage);
  for (int index = 0; index < len; ++index) {
    const std::string& s = nodes[index];
    values[index] = p;
    lengths[index] = s.size();

TFE_ContextOptions* TFE_NewContextOptions() { return new TFE_ContextOptions; }

void TFE_ContextOptionsSetConfig(TFE_ContextOptions* options, const void* proto,
                                 size_t proto_len, TF_Status* status) {
  TF_SetConfig(&options->session_options, proto, proto_len, status);
}

void TFE_ContextOptionsSetAsync(TFE_ContextOptions* options,
                                unsigned char enable) {
  options->async = enable;
}

                                                      size_t proto_len,
                                                      TF_Status* status);

// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextUpdateServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status);

 
   char *numstr = NISOBJVAL (2, obj);
   len = NISOBJLEN (2, obj);
-  if (len == 0 && numstr[len - 1] != '\0')
+  if (len == 0 || numstr[len - 1] != '\0')
     {
       if (len >= room_left)
 	goto no_more_room;
@@ -98,7 +98,7 @@ _nss_nisplus_parse_pwent (nis_result *result, struct passwd *pw,
 
   numstr = NISOBJVAL (3, obj);
   len = NISOBJLEN (3, obj);
-  if (len == 0 && numstr[len - 1] != '\0')

  TF_TString tstr[1];
  TF_TString_Init(&tstr[0]);
  TF_TString_Copy(&tstr[0], test_string, sizeof(test_string) - 1);

  auto deallocator = [](void* data, size_t len, void* arg) {};
  unique_tensor_ptr t_in(TF_NewTensor(TF_STRING, nullptr, 0, &tstr[0],
                                      sizeof(tstr), deallocator, nullptr),
                         TF_DeleteTensor);

}

const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) {
  tensorflow::mutex_lock c(graph->mu);
  const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString();
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {

          logging.exception("error importing %s", short_package_name)
          failed_packages.append(package_name)

    if failed_packages:
      self.fail(
          "Failed to import"
          f" {len(failed_packages)}/{len(self.api_packages_v2)} packages"
          f" {version}:\n{failed_packages}"
      )
    logging.info("Import of packages was successful.")


if __name__ == "__main__":