Fix for Incorrect ex_iterable used with multi num_worker (#6582)

Corrects an issue where `self._ex_iterable` was erroneously used instead of `ex_iterable`, when both Distributed Data Parallel (DDP) and multi num_worker are used concurrently. This improper usage led to the generation of incorrect `shards_indices`, subsequently causing issues with the control flow responsible for worker creation. The fix ensures the appropriate iterable is used, thus providing a more accurate determination of whether a new worker should be instantiated or not.

src/datasets/iterable_dataset.py

@@ -1275,7 +1275,7 @@ def _iter_pytorch(self):
             )
         # split workload
         _log_prefix = f"node#{self._distributed.rank} " if self._distributed else ""
-        shards_indices = self._ex_iterable.split_shard_indices_by_worker(worker_info.id, worker_info.num_workers)
+        shards_indices = ex_iterable.split_shard_indices_by_worker(worker_info.id, worker_info.num_workers)
         if shards_indices:
             logger.debug(
                 f"{_log_prefix}dataloader worker#{worker_info.id}, ': Starting to iterate over {len(shards_indices)}/{ex_iterable.n_shards} shards."