Repository: Lightning-AI/pytorch-lightning
File: src/lightning/pytorch/trainer/trainer.py (Python)

Code before:
                (``False`` if not manually set). If :paramref:`~lightning.pytorch.trainer.trainer.Trainer.deterministic`
                is set to ``True``, this will default to ``False``. Override to manually set a different value.
                Default: ``None``.

            inference_mode: Whether to use :func:`torch.inference_mode` or :func:`torch.no_grad` during
                evaluation (``validate``/``test``/``predict``).

            use_distributed_sampler: Whether to wrap the DataLoader's sampler with
                :class:`torch.utils.data.DistributedSampler`. If not specified this is toggled automatically for
                strategies that require it. By default, it will add ``shuffle=True`` for the train sampler and
                ``shuffle=False`` for validation/test/predict samplers. If you want to disable this logic, you can pass
                ``False`` and add your own distributed sampler in the dataloader hooks. If ``True`` and a distributed
                sampler was already added, Lightning will not replace the existing one. For iterable-style datasets,
                we don't do this automatically.

            profiler: To profile individual steps during training and assist in identifying bottlenecks.
                Default: ``None``.

                .. note::
                    Do **not** use a manual ``torch.profiler.profile`` context manager around
                    ``Trainer.fit()``, ``Trainer.validate()``, etc.
                    This will lead to internal errors and cryptic crashes due to incompatibility between
                    PyTorch Profiler and Lightning's training loop.
                    Always use this ``profiler`` argument to enable profiling in Lightning.


            detect_anomaly: Enable anomaly detection for the autograd engine.
                Default: ``False``.

            barebones: Whether to run in "barebones mode", where all features that may impact raw speed are
                disabled. This is meant for analyzing the Trainer overhead and is discouraged during regular training
                runs. The following features are deactivated:
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.enable_checkpointing`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.logger`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.enable_progress_bar`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.log_every_n_steps`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.enable_model_summary`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.num_sanity_val_steps`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.fast_dev_run`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.detect_anomaly`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.profiler`,
                :meth:`~lightning.pytorch.core.LightningModule.log`,
                :meth:`~lightning.pytorch.core.LightningModule.log_dict`.
            plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins.
                Default: ``None``.

            sync_batchnorm: Synchronize batch norm layers between process groups/whole world.
                Default: ``False``.

            reload_dataloaders_every_n_epochs: Set to a positive integer to reload dataloaders every n epochs.
                Default: ``0``.

Review comment (refactor):
Overall I think the intention with the rainer arg docstring is that it should be fairly short and on point. There are not a note on any of the other arguments so lets remove this.
```suggestion
```

Code after:
                (``False`` if not manually set). If :paramref:`~lightning.pytorch.trainer.trainer.Trainer.deterministic`
                is set to ``True``, this will default to ``False``. Override to manually set a different value.
                Default: ``None``.

            inference_mode: Whether to use :func:`torch.inference_mode` or :func:`torch.no_grad` during
                evaluation (``validate``/``test``/``predict``).

            use_distributed_sampler: Whether to wrap the DataLoader's sampler with
                :class:`torch.utils.data.DistributedSampler`. If not specified this is toggled automatically for
                strategies that require it. By default, it will add ``shuffle=True`` for the train sampler and
                ``shuffle=False`` for validation/test/predict samplers. If you want to disable this logic, you can pass
                ``False`` and add your own distributed sampler in the dataloader hooks. If ``True`` and a distributed
                sampler was already added, Lightning will not replace the existing one. For iterable-style datasets,
                we don't do this automatically.

            profiler: To profile individual steps during training and assist in identifying bottlenecks.
                Default: ``None``.

            detect_anomaly: Enable anomaly detection for the autograd engine.
                Default: ``False``.

            barebones: Whether to run in "barebones mode", where all features that may impact raw speed are
                disabled. This is meant for analyzing the Trainer overhead and is discouraged during regular training
                runs. The following features are deactivated:
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.enable_checkpointing`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.logger`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.enable_progress_bar`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.log_every_n_steps`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.enable_model_summary`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.num_sanity_val_steps`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.fast_dev_run`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.detect_anomaly`,
                :paramref:`~lightning.pytorch.trainer.trainer.Trainer.profiler`,
                :meth:`~lightning.pytorch.core.LightningModule.log`,
                :meth:`~lightning.pytorch.core.LightningModule.log_dict`.
            plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins.
                Default: ``None``.

            sync_batchnorm: Synchronize batch norm layers between process groups/whole world.
                Default: ``False``.

            reload_dataloaders_every_n_epochs: Set to a positive integer to reload dataloaders every n epochs.
                Default: ``0``.

            default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed.
                Default: ``os.getcwd()``.
                Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/'

            enable_autolog_hparams: Whether to log hyperparameters at the start of a run.
                Default: ``True``.