Move _TrainingEpochLoop (#16801)

Author: carmocca

src/lightning/pytorch/loops/__init__.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from lightning.pytorch.loops.loop import _Loop  # noqa: F401 isort: skip (avoids circular imports)
-from lightning.pytorch.loops.epoch import _TrainingEpochLoop  # noqa: F401
 from lightning.pytorch.loops.evaluation_loop import _EvaluationLoop  # noqa: F401
 from lightning.pytorch.loops.fit_loop import _FitLoop  # noqa: F401
 from lightning.pytorch.loops.optimization import _AutomaticOptimization, _ManualOptimization  # noqa: F401
 from lightning.pytorch.loops.prediction_loop import _PredictionLoop  # noqa: F401
+from lightning.pytorch.loops.training_epoch_loop import _TrainingEpochLoop  # noqa: F401

src/lightning/pytorch/loops/epoch/__init__.py

@@ -17,9 +17,9 @@
 import lightning.pytorch as pl
 from lightning.fabric.utilities.data import _auto_add_worker_init_fn
 from lightning.pytorch.loops import _Loop
-from lightning.pytorch.loops.epoch import _TrainingEpochLoop
 from lightning.pytorch.loops.fetchers import _DataFetcher
 from lightning.pytorch.loops.progress import Progress
+from lightning.pytorch.loops.training_epoch_loop import _TrainingEpochLoop
 from lightning.pytorch.loops.utilities import _is_max_limit_reached, _select_data_fetcher, _set_sampler_epoch
 from lightning.pytorch.trainer import call
 from lightning.pytorch.trainer.connectors.data_connector import _DataLoaderSource

src/lightning/pytorch/loops/fit_loop.py

@@ -41,13 +41,13 @@ class _TrainingEpochLoop(loops._Loop):
     them in one of these hooks, and running validation at the requested interval.
 
     The validation is carried out by yet another loop,
-    :class:`~lightning.pytorch.loops.epoch.validation_epoch_loop.ValidationEpochLoop`.
+    :class:`~lightning.pytorch.loops._EvaluationLoop`.
 
     In the ``run()`` method, the training epoch loop could in theory simply call the
     ``LightningModule.training_step`` already and perform the optimization.
     However, Lightning has built-in support for automatic optimization with multiple optimizers.
     For this reason there are actually two more loops nested under
-    :class:`~lightning.pytorch.loops.epoch.training_epoch_loop.TrainingEpochLoop`.
+    :class:`~lightning.pytorch.loops._TrainingEpochLoop`.
 
     Args:
         min_steps: The minimum number of steps (batches) to process