if (TF_GetCode(status) == TF_OK) {
          block->data.resize(bytes_transferred, 0);
          // Shrink the data capacity to the actual size used.
          // NOLINTNEXTLINE: shrink_to_fit() may not shrink the capacity.
          std::vector<char>(block->data).swap(block->data);
          downloaded_block = true;
          block->state = FetchState::FINISHED;
        } else {

*   `tf.keras`:

    *   `Model.fit` major improvements:
        *   You can now use custom training logic with `Model.fit` by overriding
            `Model.train_step`.
        *   Easily write state-of-the-art training loops without worrying about
            all of the features `Model.fit` handles for you (distribution
            strategies, callbacks, data formats, looping logic, etc)