*result = filename_;
  return OkStatus();
}

Status ModularWritableFile::Tell(int64_t* position) {
  if (ops_->tell == nullptr)
    return errors::Unimplemented(
        tensorflow::strings::StrCat("Tell() not implemented for ", filename_));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  *position = ops_->tell(file_.get(), plugin_status.get());
  return StatusFromTF_Status(plugin_status.get());
}

        layers in order to work, where the Keras symbolic shape inference proves
        insufficient.
    *   Code that tries manually walking a `tf.keras.Model` layer by layer and
        assumes layers only ever have one positional argument. This assumption
        doesn't hold true before TF 2.4 either, but is more likely to cause
        issues now.
    *   Code that manually enters `keras.backend.get_graph()` before building a

  Status Append(StringPiece data) override;
  Status Close() override;
  Status Flush() override;
  Status Sync() override;
  Status Name(StringPiece* result) const override;
  Status Tell(int64_t* position) override;

 private:
  std::string filename_;
  std::unique_ptr<TF_WritableFile> file_;
  const TF_WritableFileOps* ops_;  // not owned
  ModularWritableFile(const ModularWritableFile&) = delete;

  auto gcs_file = static_cast<GCSFile*>(file->plugin_file);
  int64_t position = int64_t(gcs_file->outfile.tellp());
  if (position == -1)
    TF_SetStatus(status, TF_INTERNAL,
                 "tellp on the internal temporary file failed");
  else
    TF_SetStatus(status, TF_OK, "");
  return position == -1
             ? -1
             : position + (gcs_file->offset == -1 ? 0 : gcs_file->offset);
}

  // POSIX's `ftell` returns `long`, do a manual cast.
  int64_t position = int64_t{ftell(posix_file->handle)};
  if (position < 0)
    TF_SetStatusFromIOError(status, errno, posix_file->filename);
  else
    TF_SetStatus(status, TF_OK, "");

  return position;
}

static void Flush(const TF_WritableFile* file, TF_Status* status) {