[pre-commit.ci] pre-commit suggestions (#19229)

* [pre-commit.ci] pre-commit suggestions

updates:
- [github.com/pre-commit/pre-commit-hooks: v4.4.0 → v4.5.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.4.0...v4.5.0)
- [github.com/asottile/pyupgrade: v3.14.0 → v3.15.0](https://github.com/asottile/pyupgrade/compare/v3.14.0...v3.15.0)
- [github.com/astral-sh/ruff-pre-commit: v0.1.3 → v0.1.9](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.3...v0.1.9)
- [github.com/psf/black: 23.9.1 → 23.12.1](https://github.com/psf/black/compare/23.9.1...23.12.1)
- [github.com/pre-commit/mirrors-prettier: v3.0.3 → v4.0.0-alpha.8](https://github.com/pre-commit/mirrors-prettier/compare/v3.0.3...v4.0.0-alpha.8)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update .pre-commit-config.yaml

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* drop unused

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: Jirka <jirka.borovec@seznam.cz>

.actions/assistant.py

@@ -234,7 +234,7 @@ def _download_frontend(pkg_path: str, version: str = "v0.0.0"):
         response = urllib.request.urlopen(frontend_release_url)
 
         file = tarfile.open(fileobj=response, mode="r|gz")
-        file.extractall(path=download_dir)
+        file.extractall(path=download_dir)  # noqa: S202
 
         shutil.move(download_dir, frontend_dir)
         print("The Lightning UI has successfully been downloaded!")
@@ -468,7 +468,7 @@ class AssistantCLI:
                 raise RuntimeError(f"Requesting file '{zip_url}' does not exist or it is just unavailable.")
 
             with zipfile.ZipFile(zip_file, "r") as zip_ref:
-                zip_ref.extractall(tmp)
+                zip_ref.extractall(tmp)  # noqa: S202
 
             zip_dirs = [d for d in glob.glob(os.path.join(tmp, "*")) if os.path.isdir(d)]
             # check that the extracted archive has only repo folder

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -51,7 +51,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.14.0
+    rev: v3.15.0
     hooks:
       - id: pyupgrade
         args: ["--py38-plus"]
@@ -84,13 +84,13 @@ repos:
           - flake8-return
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.3"
+    rev: "v0.1.9"
     hooks:
       - id: ruff
         args: ["--fix", "--preview"]
 
   - repo: https://github.com/psf/black
-    rev: 23.9.1
+    rev: 23.12.1
     hooks:
       - id: black
         name: Format code
@@ -121,7 +121,7 @@ repos:
           )$
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.3
+    rev: v3.1.0
     hooks:
       - id: prettier
         # https://prettier.io/docs/en/options.html#print-width

examples/app/hpo/utils.py

@@ -48,7 +48,7 @@ def download_data(url: str, path: str = "data/", verbose: bool = False) -> None:
     if ".zip" in local_filename:
         if os.path.exists(local_filename):
             with zipfile.ZipFile(local_filename, "r") as zip_ref:
-                zip_ref.extractall(path)
+                zip_ref.extractall(path)  # noqa: S202
     elif local_filename.endswith(".tar.gz") or local_filename.endswith(".tgz"):
         extract_tarfile(local_filename, path, "r:gz")
     elif local_filename.endswith(".tar.bz2") or local_filename.endswith(".tbz"):

examples/fabric/reinforcement_learning/rl/utils.py

@@ -173,7 +173,7 @@ def test(
         action = agent.get_greedy_action(next_obs)
 
         # Single environment step
-        next_obs, reward, done, truncated, info = env.step(action.cpu().numpy())
+        next_obs, reward, done, truncated, _ = env.step(action.cpu().numpy())
         done = done or truncated
         cumulative_rew += reward
         next_obs = torch.tensor(next_obs, device=device)

examples/pytorch/basics/backbone_image_classifier.py

@@ -92,7 +92,7 @@ class LitClassifier(LightningModule):
         self.log("test_loss", loss)
 
     def predict_step(self, batch, batch_idx, dataloader_idx=None):
-        x, y = batch
+        x, _ = batch
         return self(x)
 
     def configure_optimizers(self):

pyproject.toml

@@ -107,6 +107,7 @@ ignore-init-module-imports = true
     "S113",  # todo: Probable use of requests call without timeout
     "S311",  # todo: Standard pseudo-random generators are not suitable for cryptographic purposes
     "S108",  # todo: Probable insecure usage of temporary file or directory: "/tmp/sys-customizations-sync"
+    "S202",  # Uses of `tarfile.extractall()`
     "S602",  # todo: `subprocess` call with `shell=True` identified, security issue
     "S603",  # todo: `subprocess` call: check for execution of untrusted input
     "S605",  # todo: Starting a process with a shell: seems safe, but may be changed in the future; consider rewriting without `shell`

src/lightning/app/cli/cmd_init.py

@@ -27,7 +27,7 @@ def app(app_name: str) -> None:
         app_name = _capture_valid_app_component_name(resource_type="app")
 
     # generate resource template
-    new_resource_name, name_for_files = _make_resource(resource_dir="app-template", resource_name=app_name)
+    new_resource_name, _ = _make_resource(resource_dir="app-template", resource_name=app_name)
 
     m = f"""
     ⚡ Lightning app template created! ⚡

src/lightning/app/cli/cmd_pl_init.py

@@ -121,7 +121,7 @@ def download_frontend(destination: Path) -> None:
     with TemporaryDirectory() as download_dir:
         response = urllib.request.urlopen(url)  # noqa: S310
         file = tarfile.open(fileobj=response, mode="r|gz")
-        file.extractall(path=download_dir)
+        file.extractall(path=download_dir)  # noqa: S202
         shutil.move(str(Path(download_dir, build_dir_name)), destination)
 
 

src/lightning/app/plugin/plugin.py

@@ -154,7 +154,7 @@ def _run_plugin(run: _Run) -> Dict[str, Any]:
             logger.info("Extracting plugin source.")
 
             with tarfile.open(download_path, "r:gz") as tf:
-                tf.extractall(source_path)
+                tf.extractall(source_path)  # noqa: S202
         except Exception as ex:
             raise HTTPException(
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,

src/lightning/app/storage/drive.py

@@ -334,7 +334,7 @@ class Drive:
 
 
 def _maybe_create_drive(component_name: str, state: Dict) -> Union[Dict, Drive]:
-    if state.get("type", None) == Drive.__IDENTIFIER__:
+    if state.get("type") == Drive.__IDENTIFIER__:
         drive = Drive.from_dict(state)
         drive.component_name = component_name
         return drive

src/lightning/app/utilities/packaging/cloud_compute.py

@@ -156,7 +156,7 @@ class CloudCompute:
                 f"mounts argument must be one of [None, Mount, List[Mount]], "
                 f"received {mounts} of type {type(mounts)}"
             )
-        _verify_mount_root_dirs_are_unique(d.get("mounts", None))
+        _verify_mount_root_dirs_are_unique(d.get("mounts"))
         return cls(**d)
 
     @property
@@ -183,6 +183,6 @@ def _verify_mount_root_dirs_are_unique(mounts: Union[None, Mount, List[Mount], T
 
 
 def _maybe_create_cloud_compute(state: Dict) -> Union[CloudCompute, Dict]:
-    if state and state.get("type", None) == __CLOUD_COMPUTE_IDENTIFIER__:
+    if state and state.get("type") == __CLOUD_COMPUTE_IDENTIFIER__:
         return CloudCompute.from_dict(state)
     return state

src/lightning/app/utilities/packaging/lightning_utils.py

@@ -52,7 +52,7 @@ def download_frontend(root: str = _PROJECT_ROOT):
     response = urllib.request.urlopen(LIGHTNING_FRONTEND_RELEASE_URL)  # noqa: S310
 
     file = tarfile.open(fileobj=response, mode="r|gz")
-    file.extractall(path=download_dir)
+    file.extractall(path=download_dir)  # noqa: S202
 
     shutil.move(os.path.join(download_dir, build_dir), frontend_dir)
     print("The Lightning UI has successfully been downloaded!")

src/lightning/data/streaming/data_processor.py

@@ -258,7 +258,7 @@ def _get_item_filesizes(items: List[Any], base_path: str = "") -> List[int]:
     """Computes the total size in bytes of all file paths for every datastructure in the given list."""
     item_sizes = []
     for item in items:
-        flattened_item, spec = tree_flatten(item)
+        flattened_item, _ = tree_flatten(item)
 
         num_bytes = 0
         for index, element in enumerate(flattened_item):

src/lightning/fabric/plugins/precision/bitsandbytes.py

@@ -252,7 +252,7 @@ def _import_bitsandbytes() -> ModuleType:
             if int8params.has_fp16_weights:
                 int8params.data = B
             else:
-                CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
+                CB, CBt, SCB, SCBt, _ = bnb.functional.double_quant(B)
                 del CBt
                 del SCBt
                 int8params.data = CB

src/lightning/fabric/strategies/launchers/subprocess_script.py

@@ -163,10 +163,11 @@ def _basic_subprocess_cmd() -> Sequence[str]:
 
 
 def _hydra_subprocess_cmd(local_rank: int) -> Tuple[Sequence[str], str]:
-    import __main__  # local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
     from hydra.core.hydra_config import HydraConfig
     from hydra.utils import get_original_cwd, to_absolute_path
 
+    import __main__  # local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
+
     # when user is using hydra find the absolute path
     if __main__.__spec__ is None:  # pragma: no-cover
         command = [sys.executable, to_absolute_path(sys.argv[0])]

src/lightning/fabric/utilities/load.py

@@ -109,7 +109,7 @@ class _NotYetLoadedTensor:
     def _load_tensor(self) -> Tensor:
         from torch.storage import TypedStorage, UntypedStorage
 
-        name, storage_cls, fn, device, size = self.storageinfo
+        _, _, fn, _, size = self.storageinfo
         dtype = self.metatensor.dtype
 
         storage = self.archiveinfo.file_reader.get_storage_from_record(
@@ -182,7 +182,7 @@ class _LazyLoadingUnpickler(pickle.Unpickler):
     def persistent_load(self, pid: tuple) -> "TypedStorage":
         from torch.storage import TypedStorage
 
-        name, cls, fn, device, size = pid
+        _, cls, _, _, _ = pid
         with warnings.catch_warnings():
             # The TypedStorage APIs have heavy deprecations in torch, suppress all these warnings for now
             warnings.simplefilter("ignore")

src/lightning/pytorch/callbacks/pruning.py

@@ -302,7 +302,7 @@ class ModelPruning(Callback):
                 copy = deepcopy(copy)  # keep the original parameters
                 copy.reset_parameters()
             for i, name in names:
-                new, new_name = self._parameters_to_prune[i]
+                new, _ = self._parameters_to_prune[i]
                 self._copy_param(new, copy, name)
 
     def _apply_local_pruning(self, amount: float) -> None:

src/lightning/pytorch/demos/transformer.py

@@ -53,7 +53,7 @@ class Transformer(nn.Module):
         self.src_mask = None
 
     def forward(self, inputs: Tensor, target: Tensor, mask: Optional[Tensor] = None) -> Tensor:
-        b, t = inputs.shape
+        _, t = inputs.shape
 
         # we assume target is already shifted w.r.t. inputs
         if mask is None:

src/lightning/pytorch/utilities/_pytree.py

@@ -11,7 +11,7 @@ def _is_leaf_or_primitive_container(pytree: PyTree) -> bool:
 
     node_type = _get_node_type(pytree)
     flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
-    child_pytrees, context = flatten_fn(pytree)
+    child_pytrees, _ = flatten_fn(pytree)
     return all(isinstance(child, (int, float, str)) for child in child_pytrees)
 
 

tests/integrations_app/flagship/test_flashy.py

@@ -69,5 +69,5 @@ def validate_app_functionalities(app_page: "Page") -> None:
 
 @pytest.mark.cloud()
 def test_app_cloud() -> None:
-    with run_app_in_cloud(_PATH_INTEGRATIONS_DIR) as (admin_page, view_page, fetch_logs, _):
+    with run_app_in_cloud(_PATH_INTEGRATIONS_DIR) as (_, view_page, _, _):
         validate_app_functionalities(view_page)

tests/integrations_app/public/test_v0_app.py

@@ -68,7 +68,7 @@ def test_v0_app_example_byoc_cloud() -> None:
     with run_app_in_cloud(
         os.path.join(_PATH_EXAMPLES, "v0"),
         extra_args=["--cluster-id", os.environ.get("LIGHTNING_BYOC_CLUSTER_ID")],
-    ) as (_, view_page, fetch_logs, app_name):
+    ) as (_, view_page, fetch_logs, _):
         run_v0_app(fetch_logs, view_page)
 
 

tests/tests_app/components/multi_node/test_fabric.py

@@ -57,7 +57,7 @@ def test_fabric_run_executor_mps_forced_cpu(accelerator_given, accelerator_expec
         warning_context = no_warning_call(match=warning_str + "*")
 
     with warning_context:
-        ret_val, env_vars = _get_args_after_tracer_injection(accelerator=accelerator_given)
+        ret_val, _ = _get_args_after_tracer_injection(accelerator=accelerator_given)
     assert ret_val["accelerator"] == accelerator_expected
 
 

tests/tests_app/components/multi_node/test_trainer.py

@@ -52,7 +52,7 @@ def test_trainer_run_executor_mps_forced_cpu(accelerator_given, accelerator_expe
         warning_context = no_warning_call(match=warning_str + "*")
 
     with warning_context:
-        ret_val, env_vars = _get_args_after_tracer_injection(accelerator=accelerator_given)
+        ret_val, _ = _get_args_after_tracer_injection(accelerator=accelerator_given)
     assert ret_val["accelerator"] == accelerator_expected
 
 

tests/tests_app/core/test_lightning_app.py

@@ -1115,7 +1115,7 @@ class FlowWrapper(LightningFlow):
 def test_cloud_compute_binding():
     cloud_compute.ENABLE_MULTIPLE_WORKS_IN_NON_DEFAULT_CONTAINER = True
 
-    assert {} == cloud_compute._CLOUD_COMPUTE_STORE
+    assert cloud_compute._CLOUD_COMPUTE_STORE == {}
     flow = FlowCC()
     assert len(cloud_compute._CLOUD_COMPUTE_STORE) == 2
     assert cloud_compute._CLOUD_COMPUTE_STORE["default"].component_names == ["root.work_c"]

tests/tests_app/runners/test_cloud.py

@@ -550,14 +550,14 @@ class TestAppCreationClient:
 
         # testing with no-cache False
         cloud_runtime.dispatch(no_cache=False)
-        (func_name, args, kwargs) = cloud_runtime.backend.client.cloud_space_service_create_lightning_run.mock_calls[0]
+        _, _, kwargs = cloud_runtime.backend.client.cloud_space_service_create_lightning_run.mock_calls[0]
         body = kwargs["body"]
         assert body.dependency_cache_key == "dummy-hash"
 
         # testing with no-cache True
         mock_client.reset_mock()
         cloud_runtime.dispatch(no_cache=True)
-        (func_name, args, kwargs) = cloud_runtime.backend.client.cloud_space_service_create_lightning_run.mock_calls[0]
+        _, _, kwargs = cloud_runtime.backend.client.cloud_space_service_create_lightning_run.mock_calls[0]
         body = kwargs["body"]
         assert body.dependency_cache_key is None
 

tests/tests_fabric/utilities/test_data.py

@@ -560,7 +560,7 @@ def test_dataloader_kwargs_replacement_with_array_default_comparison():
             self.indices = np.random.rand(2, 2)  # an attribute we can't compare with ==
 
     dataloader = ArrayAttributeDataloader(dataset)
-    dl_args, dl_kwargs = _get_dataloader_init_args_and_kwargs(dataloader, dataloader.sampler)
+    _, dl_kwargs = _get_dataloader_init_args_and_kwargs(dataloader, dataloader.sampler)
     assert dl_kwargs["indices"] is dataloader.indices
 
 

tests/tests_pytorch/callbacks/progress/test_rich_progress_bar.py

@@ -117,17 +117,17 @@ def test_rich_progress_bar_custom_theme():
         progress_bar.on_train_start(Trainer(), BoringModel())
 
         assert progress_bar.theme == theme
-        args, kwargs = mocks["CustomBarColumn"].call_args
+        _, kwargs = mocks["CustomBarColumn"].call_args
         assert kwargs["complete_style"] == theme.progress_bar
         assert kwargs["finished_style"] == theme.progress_bar_finished
 
-        args, kwargs = mocks["BatchesProcessedColumn"].call_args
+        _, kwargs = mocks["BatchesProcessedColumn"].call_args
         assert kwargs["style"] == theme.batch_progress
 
-        args, kwargs = mocks["CustomTimeColumn"].call_args
+        _, kwargs = mocks["CustomTimeColumn"].call_args
         assert kwargs["style"] == theme.time
 
-        args, kwargs = mocks["ProcessingSpeedColumn"].call_args
+        _, kwargs = mocks["ProcessingSpeedColumn"].call_args
         assert kwargs["style"] == theme.processing_speed
 
 

tests/tests_pytorch/callbacks/test_rich_model_summary.py

@@ -61,5 +61,5 @@ def test_rich_summary_tuples(mock_table_add_row, mock_console):
     # ensure that summary was logged + the breakdown of model parameters
     assert mock_console.call_count == 2
     # assert that the input summary data was converted correctly
-    args, kwargs = mock_table_add_row.call_args_list[0]
+    args, _ = mock_table_add_row.call_args_list[0]
     assert args[1:] == ("0", "layer", "Linear", "66  ", "[4, 32]", "[4, 2]")

tests/tests_pytorch/helpers/advanced_models.py

@@ -171,7 +171,7 @@ class ParityModuleRNN(LightningModule):
         self._loss = []  # needed for checking if the loss is the same as vanilla torch
 
     def forward(self, x):
-        seq, last = self.rnn(x)
+        seq, _ = self.rnn(x)
         return self.linear_out(seq)
 
     def training_step(self, batch, batch_nb):

tests/tests_pytorch/loops/test_fetchers.py

@@ -179,9 +179,9 @@ def test_fetching_dataloader_iter_opt(automatic_optimization, tmpdir):
         def training_step(self, dataloader_iter):
             assert isinstance(self.trainer.fit_loop._data_fetcher, _DataLoaderIterDataFetcher)
             # fetch 2 batches
-            batch, batch_idx, dataloader_idx = next(dataloader_iter)
+            batch, batch_idx, _ = next(dataloader_iter)
             self.batches.append(batch)
-            batch, batch_idx, dataloader_idx = next(dataloader_iter)
+            batch, batch_idx, _ = next(dataloader_iter)
             self.batches.append(batch)
 
             batch = self.batches.pop(0)
@@ -216,7 +216,7 @@ def test_fetching_dataloader_iter_running_stages(fn, tmp_path):
     class TestModel(BoringModel):
         def fetch(self, data_fetcher, dataloader_iter):
             assert isinstance(data_fetcher, _DataLoaderIterDataFetcher)
-            batch, batch_idx, dataloader_idx = next(dataloader_iter)
+            batch, batch_idx, _ = next(dataloader_iter)
             assert data_fetcher.fetched == batch_idx + 1
             return batch
 

tests/tests_pytorch/models/test_fabric_integration.py

@@ -51,7 +51,7 @@ def test_fabric_boring_lightning_module_manual():
     optimizers, _ = module.configure_optimizers()
     dataloader = module.train_dataloader()
 
-    model, optimizer = fabric.setup(module, optimizers[0])
+    model, _ = fabric.setup(module, optimizers[0])
     dataloader = fabric.setup_dataloaders(dataloader)
 
     batch = next(iter(dataloader))

tests/tests_pytorch/models/test_onnx.py

@@ -111,8 +111,8 @@ def test_verbose_param(tmpdir, capsys):
 
     with patch("torch.onnx.log", autospec=True) as test:
         model.to_onnx(file_path, verbose=True)
-    args, kwargs = test.call_args
-    prefix, graph = args
+    args, _ = test.call_args
+    prefix, _ = args
     assert prefix == "Exported graph: "
 
 

tests/tests_pytorch/plugins/precision/test_double.py

@@ -39,7 +39,7 @@ class RandomFloatIntDataset(Dataset):
 
 class DoublePrecisionBoringModel(BoringModel):
     def training_step(self, batch, batch_idx):
-        float_data, int_data = batch
+        float_data, _ = batch
         assert torch.tensor([0.0]).dtype == torch.float64
         assert torch.tensor([0.0], dtype=torch.float16).dtype == torch.float16
         assert float_data.dtype == torch.float64

tests/tests_pytorch/plugins/test_amp_plugins.py

@@ -169,7 +169,7 @@ def test_amp_skip_optimizer(tmpdir):
             return x
 
         def training_step(self, batch, batch_idx):
-            opt1, opt2 = self.optimizers()
+            _, opt2 = self.optimizers()
             output = self(batch)
             loss = self.loss(output)
             opt2.zero_grad()

tests/tests_pytorch/strategies/test_fsdp.py

@@ -114,7 +114,7 @@ class TestBoringModel(BoringModel):
 
         self.save_hyperparameters()
         self.layer = torch.nn.Sequential(torch.nn.Linear(32, 32), torch.nn.ReLU(), torch.nn.Linear(32, 2))
-        self.should_be_wrapped = [(32 * 32 + 32) > wrap_min_params, None, (32 * 2 + 2) > wrap_min_params]
+        self.should_be_wrapped = [wrap_min_params < (32 * 32 + 32), None, wrap_min_params < (32 * 2 + 2)]
 
     def configure_optimizers(self):
         parameters = self.parameters() if _TORCH_GREATER_EQUAL_2_0 else self.trainer.model.parameters()

tests/tests_pytorch/trainer/logging_/test_eval_loop_logging.py

@@ -851,7 +851,7 @@ def test_native_print_results_encodings(monkeypatch, encoding):
 
     # Attempt to encode everything the file is told to write with the given encoding
     for call_ in out.method_calls:
-        name, args, kwargs = call_
+        name, args, _ = call_
         if name == "write":
             args[0].encode(encoding)
 

tests/tests_pytorch/trainer/optimization/test_manual_optimization.py

@@ -610,12 +610,12 @@ class TesManualOptimizationDDPModel(BoringModel):
                 assert torch.equal(self.layer.weight.grad, grad_clone)
 
         def gen_closure():
-            loss_ones_gen, loss_zeros = compute_loss()
+            loss_ones_gen, _ = compute_loss()
             make_manual_backward(loss_ones_gen, retain_graph=True, make_optimizer_step=make_gen_optimizer_step)
             make_manual_backward(loss_ones_gen, make_optimizer_step=make_gen_optimizer_step)
 
         def dis_closure():
-            loss_ones_gen, loss_zeros = compute_loss()
+            loss_ones_gen, _ = compute_loss()
             make_manual_backward(loss_ones_gen, retain_graph=True, make_optimizer_step=make_dis_optimizer_step)
             make_manual_backward(loss_ones_gen, make_optimizer_step=make_dis_optimizer_step)
 
@@ -719,12 +719,12 @@ class TestManualOptimizationDDPModelToggleModel(TesManualOptimizationDDPModel):
                 assert torch.equal(self.layer.weight.grad, grad_clone)
 
         def gen_closure():
-            loss_ones_gen, loss_zeros = compute_loss()
+            loss_ones_gen, _ = compute_loss()
             make_manual_backward(loss_ones_gen, retain_graph=True, make_optimizer_step=make_gen_optimizer_step)
             make_manual_backward(loss_ones_gen, make_optimizer_step=make_gen_optimizer_step)
 
         def dis_closure():
-            loss_ones_gen, loss_zeros = compute_loss()
+            loss_ones_gen, _ = compute_loss()
             make_manual_backward(loss_ones_gen, retain_graph=True, make_optimizer_step=make_dis_optimizer_step)
             make_manual_backward(loss_ones_gen, make_optimizer_step=make_dis_optimizer_step)
 

tests/tests_pytorch/utilities/test_data.py

@@ -324,5 +324,5 @@ def test_dataloader_kwargs_replacement_with_array_default_comparison():
             self.indices = np.random.rand(2, 2)  # an attribute we can't compare with ==
 
     dataloader = ArrayAttributeDataloader(dataset)
-    dl_args, dl_kwargs = _get_dataloader_init_args_and_kwargs(dataloader, dataloader.sampler)
+    _, dl_kwargs = _get_dataloader_init_args_and_kwargs(dataloader, dataloader.sampler)
     assert dl_kwargs["indices"] is dataloader.indices