std::unique_ptr<const void*[]>* ptrs,
                          std::unique_ptr<size_t[]>* lens) {
  ptrs->reset(new const void*[v.size()]);
  lens->reset(new size_t[v.size()]);
  for (size_t i = 0; i < v.size(); ++i) {
    (*ptrs)[i] = v[i].data();
    (*lens)[i] = v[i].size();
  }
}

class CApiColocationTest : public ::testing::Test {
 protected:

  void* data = tensorflow::port::Malloc(value.length());
  value.copy(static_cast<char*>(data), value.length(), 0);
  buf->data = data;
  buf->length = value.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };
}

TFE_MonitoringStringGauge0* TFE_MonitoringNewStringGauge0(
    const char* name, TF_Status* status, const char* description) {

}

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) {

  const auto& s = attr->s();
  std::memcpy(value, s.data(), std::min<size_t>(s.length(), max_length));
}

void TF_OperationGetAttrStringList(TF_Operation* oper, const char* attr_name,
                                   void** values, size_t* lengths,
                                   int max_values, void* storage,
                                   size_t storage_size, TF_Status* status) {

    fprintf(stderr, "TF_GetLocalTempDirectories returned no results\n");
    return 1;
  }

  char file_name[100];
  time_t t = time(NULL);
  snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t);

  size_t length = 2 + strlen(path) + strlen(file_name);
  char* full_path = malloc(length);
  snprintf(full_path, length, "%s/%s", path, file_name);

  TF_WritableFileHandle* h;

                              TF_Status* status) {
  status->status = MessageToBuffer(func->record->fdef(), output_func_def);
}

TF_Function* TF_FunctionImportFunctionDef(const void* proto, size_t proto_len,
                                          TF_Status* status) {
  tensorflow::FunctionDef fdef;
  bool success = fdef.ParseFromArray(proto, proto_len);
  if (!success) {
    status->status = InvalidArgument(

	if written < fileSize && fileSize >= 0 {
		w.Close()
		return errLessData
	} else if written > fileSize && fileSize >= 0 {
		w.Close()
		return errMoreData
	}

	// Only interested in flushing the size_t not mtime/atime
	if err = Fdatasync(w); err != nil {
		w.Close()
		return err
	}

	// Dealing with error returns from close() - 'man 2 close'
	//

              <excludes>META-INF/MANIFEST.MF</excludes>
              <outputDirectory>${project.build.directory}/failureaccess-sources</outputDirectory>
              <type>java-source</type>
              <silent>false</silent>
            </configuration>
          </execution>
          <execution>
            <id>unpack-guava-sources</id>
            <phase>generate-resources</phase>

type BytePoolCap struct {
	c    chan []byte
	w    int
	wcap int
}

// NewBytePoolCap creates a new BytePool bounded to the given maxSize, with new
// byte arrays sized based on width.
func NewBytePoolCap(maxSize uint64, width int, capwidth int) (bp *BytePoolCap) {
	if capwidth > 0 && capwidth < 64 {
		panic("buffer capped with smaller than 64 bytes is not supported")
	}

			sizeS.failedCount++
		case replication.Completed, replication.CompletedLegacy:
			tgtSizeS.replicatedSize += oi.Size
			tgtSizeS.replicatedCount++
			sizeS.replicatedSize += oi.Size
			sizeS.replicatedCount++
		}
		sizeS.replTargetStats[arn] = tgtSizeS
	}

	if oi.ReplicationStatus == replication.Replica {
		sizeS.replicaSize += oi.Size
		sizeS.replicaCount++
	}
}