FIX remove xfail_checks

[PSSF23]

According to errors in neurodata/treeple#361, xfail_checks is not available in the tags.

[github-actions]

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---

ruff

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--- build_tools/circle/list_versions.py
+++ build_tools/circle/list_versions.py
@@ -71,9 +71,7 @@
     "Web-based documentation is available for versions listed below:\n",
 ]
 
-ROOT_URL = (
-    "https://api.github.com/repos/scikit-learn/scikit-learn.github.io/contents/"  # noqa
-)
+ROOT_URL = "https://api.github.com/repos/scikit-learn/scikit-learn.github.io/contents/"  # noqa
 RAW_FMT = "https://raw.githubusercontent.com/scikit-learn/scikit-learn.github.io/master/%s/index.html"  # noqa
 VERSION_RE = re.compile(r"scikit-learn ([\w\.\-]+) documentation</title>")
 NAMED_DIRS = ["dev", "stable"]

--- examples/applications/plot_species_distribution_modeling.py
+++ examples/applications/plot_species_distribution_modeling.py
@@ -109,7 +109,7 @@
 
 
 def plot_species_distribution(
-    species=("bradypus_variegatus_0", "microryzomys_minutus_0")
+    species=("bradypus_variegatus_0", "microryzomys_minutus_0"),
 ):
     """
     Plot the species distribution.

--- examples/ensemble/plot_bias_variance.py
+++ examples/ensemble/plot_bias_variance.py
@@ -177,8 +177,8 @@
 
     plt.subplot(2, n_estimators, n_estimators + n + 1)
     plt.plot(X_test, y_error, "r", label="$error(x)$")
-    plt.plot(X_test, y_bias, "b", label="$bias^2(x)$"),
-    plt.plot(X_test, y_var, "g", label="$variance(x)$"),
+    (plt.plot(X_test, y_bias, "b", label="$bias^2(x)$"),)
+    (plt.plot(X_test, y_var, "g", label="$variance(x)$"),)
     plt.plot(X_test, y_noise, "c", label="$noise(x)$")
 
     plt.xlim([-5, 5])

--- examples/linear_model/plot_tweedie_regression_insurance_claims.py
+++ examples/linear_model/plot_tweedie_regression_insurance_claims.py
@@ -604,8 +604,9 @@
             "predicted, frequency*severity model": np.sum(
                 exposure * glm_freq.predict(X) * glm_sev.predict(X)
             ),
-            "predicted, tweedie, power=%.2f"
-            % glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)),
+            "predicted, tweedie, power=%.2f" % glm_pure_premium.power: np.sum(
+                exposure * glm_pure_premium.predict(X)
+            ),
         }
     )
 

--- examples/manifold/plot_lle_digits.py
+++ examples/manifold/plot_lle_digits.py
@@ -10,7 +10,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 # %%
 # Load digits dataset
 # -------------------

--- examples/manifold/plot_manifold_sphere.py
+++ examples/manifold/plot_manifold_sphere.py
@@ -50,7 +50,7 @@
 t = random_state.rand(n_samples) * np.pi
 
 # Sever the poles from the sphere.
-indices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))
+indices = (t < (np.pi - (np.pi / 8))) & (t > (np.pi / 8))
 colors = p[indices]
 x, y, z = (
     np.sin(t[indices]) * np.cos(p[indices]),

--- sklearn/_loss/tests/test_loss.py
+++ sklearn/_loss/tests/test_loss.py
@@ -215,7 +215,8 @@
 
 
 @pytest.mark.parametrize(
-    "loss, y_pred_success, y_pred_fail", Y_COMMON_PARAMS + Y_PRED_PARAMS  # type: ignore
+    "loss, y_pred_success, y_pred_fail",
+    Y_COMMON_PARAMS + Y_PRED_PARAMS,  # type: ignore
 )
 def test_loss_boundary_y_pred(loss, y_pred_success, y_pred_fail):
     """Test boundaries of y_pred for loss functions."""
@@ -501,12 +502,14 @@
         sample_weight=sample_weight,
         loss_out=out_l1,
     )
-    loss.closs.loss(
-        y_true=y_true,
-        raw_prediction=raw_prediction,
-        sample_weight=sample_weight,
-        loss_out=out_l2,
-    ),
+    (
+        loss.closs.loss(
+            y_true=y_true,
+            raw_prediction=raw_prediction,
+            sample_weight=sample_weight,
+            loss_out=out_l2,
+        ),
+    )
     assert_allclose(out_l1, out_l2)
     loss.gradient(
         y_true=y_true,

--- sklearn/cluster/_feature_agglomeration.py
+++ sklearn/cluster/_feature_agglomeration.py
@@ -6,7 +6,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import numpy as np
 from scipy.sparse import issparse
 

--- sklearn/cross_decomposition/tests/test_pls.py
+++ sklearn/cross_decomposition/tests/test_pls.py
@@ -404,12 +404,12 @@
 
     X_orig = X.copy()
     with pytest.raises(AssertionError):
-        pls.transform(X, Y, copy=False),
+        (pls.transform(X, Y, copy=False),)
         assert_array_almost_equal(X, X_orig)
 
     X_orig = X.copy()
     with pytest.raises(AssertionError):
-        pls.predict(X, copy=False),
+        (pls.predict(X, copy=False),)
         assert_array_almost_equal(X, X_orig)
 
     # Make sure copy=True gives same transform and predictions as predict=False

--- sklearn/ensemble/_bagging.py
+++ sklearn/ensemble/_bagging.py
@@ -3,7 +3,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import itertools
 import numbers
 from abc import ABCMeta, abstractmethod

--- sklearn/ensemble/_forest.py
+++ sklearn/ensemble/_forest.py
@@ -35,7 +35,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import threading
 from abc import ABCMeta, abstractmethod
 from numbers import Integral, Real

--- sklearn/ensemble/tests/test_forest.py
+++ sklearn/ensemble/tests/test_forest.py
@@ -282,11 +282,12 @@
     reg = ForestRegressor(n_estimators=5, criterion=criterion, random_state=1)
     reg.fit(X_reg, y_reg)
     score = reg.score(X_reg, y_reg)
-    assert (
-        score > 0.93
-    ), "Failed with max_features=None, criterion %s and score = %f" % (
-        criterion,
-        score,
+    assert score > 0.93, (
+        "Failed with max_features=None, criterion %s and score = %f"
+        % (
+            criterion,
+            score,
+        )
     )
 
     reg = ForestRegressor(
@@ -1858,11 +1859,12 @@
     )
     reg.fit(X_reg, y_reg)
     score = reg.score(X_reg, y_reg)
-    assert (
-        score > 0.93
-    ), "Failed with max_features=None, criterion %s and score = %f" % (
-        criterion,
-        score,
+    assert score > 0.93, (
+        "Failed with max_features=None, criterion %s and score = %f"
+        % (
+            criterion,
+            score,
+        )
     )
 
     reg = ForestRegressor(

--- sklearn/experimental/enable_hist_gradient_boosting.py
+++ sklearn/experimental/enable_hist_gradient_boosting.py
@@ -13,7 +13,6 @@
 # Don't remove this file, we don't want to break users code just because the
 # feature isn't experimental anymore.
 
-
 import warnings
 
 warnings.warn(

--- sklearn/feature_selection/_univariate_selection.py
+++ sklearn/feature_selection/_univariate_selection.py
@@ -3,7 +3,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import warnings
 from numbers import Integral, Real
 

--- sklearn/gaussian_process/tests/test_gpc.py
+++ sklearn/gaussian_process/tests/test_gpc.py
@@ -147,8 +147,9 @@
     # Define a dummy optimizer that simply tests 10 random hyperparameters
     def optimizer(obj_func, initial_theta, bounds):
         rng = np.random.RandomState(global_random_seed)
-        theta_opt, func_min = initial_theta, obj_func(
-            initial_theta, eval_gradient=False
+        theta_opt, func_min = (
+            initial_theta,
+            obj_func(initial_theta, eval_gradient=False),
         )
         for _ in range(10):
             theta = np.atleast_1d(

--- sklearn/gaussian_process/tests/test_gpr.py
+++ sklearn/gaussian_process/tests/test_gpr.py
@@ -394,8 +394,9 @@
     # Define a dummy optimizer that simply tests 50 random hyperparameters
     def optimizer(obj_func, initial_theta, bounds):
         rng = np.random.RandomState(0)
-        theta_opt, func_min = initial_theta, obj_func(
-            initial_theta, eval_gradient=False
+        theta_opt, func_min = (
+            initial_theta,
+            obj_func(initial_theta, eval_gradient=False),
         )
         for _ in range(50):
             theta = np.atleast_1d(

--- sklearn/linear_model/_linear_loss.py
+++ sklearn/linear_model/_linear_loss.py
@@ -537,9 +537,9 @@
                 # The L2 penalty enters the Hessian on the diagonal only. To add those
                 # terms, we use a flattened view of the array.
                 order = "C" if hess.flags.c_contiguous else "F"
-                hess.reshape(-1, order=order)[
-                    : (n_features * n_dof) : (n_dof + 1)
-                ] += l2_reg_strength
+                hess.reshape(-1, order=order)[: (n_features * n_dof) : (n_dof + 1)] += (
+                    l2_reg_strength
+                )
 
             if self.fit_intercept:
                 # With intercept included as added column to X, the hessian becomes

--- sklearn/linear_model/_ridge.py
+++ sklearn/linear_model/_ridge.py
@@ -5,7 +5,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import numbers
 import warnings
 from abc import ABCMeta, abstractmethod

--- sklearn/linear_model/_theil_sen.py
+++ sklearn/linear_model/_theil_sen.py
@@ -5,7 +5,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import warnings
 from itertools import combinations
 from numbers import Integral, Real

--- sklearn/manifold/_spectral_embedding.py
+++ sklearn/manifold/_spectral_embedding.py
@@ -3,7 +3,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import warnings
 from numbers import Integral, Real
 

--- sklearn/metrics/_classification.py
+++ sklearn/metrics/_classification.py
@@ -10,7 +10,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import warnings
 from numbers import Integral, Real
 

--- sklearn/metrics/_ranking.py
+++ sklearn/metrics/_ranking.py
@@ -10,7 +10,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import warnings
 from functools import partial
 from numbers import Integral, Real

--- sklearn/metrics/cluster/_supervised.py
+++ sklearn/metrics/cluster/_supervised.py
@@ -7,7 +7,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import warnings
 from math import log
 from numbers import Real

--- sklearn/metrics/cluster/_unsupervised.py
+++ sklearn/metrics/cluster/_unsupervised.py
@@ -3,7 +3,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import functools
 from numbers import Integral
 

--- sklearn/metrics/tests/test_common.py
+++ sklearn/metrics/tests/test_common.py
@@ -976,7 +976,8 @@
 @pytest.mark.parametrize("metric", CLASSIFICATION_METRICS.values())
 @pytest.mark.parametrize(
     "y_true, y_score",
-    invalids_nan_inf +
+    invalids_nan_inf
+    +
     # Add an additional case for classification only
     # non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/6809
@@ -2006,7 +2007,6 @@
 
 
 def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name):
-
     X_np = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=dtype_name)
     Y_np = np.array([[0.2, 0.3, 0.4], [0.5, 0.6, 0.7]], dtype=dtype_name)
 

--- sklearn/model_selection/_validation.py
+++ sklearn/model_selection/_validation.py
@@ -6,7 +6,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 import numbers
 import time
 import warnings

--- sklearn/multioutput.py
+++ sklearn/multioutput.py
@@ -8,7 +8,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 from abc import ABCMeta, abstractmethod
 from numbers import Integral
 

--- sklearn/neighbors/tests/test_neighbors.py
+++ sklearn/neighbors/tests/test_neighbors.py
@@ -655,10 +655,12 @@
             assert_allclose(np.concatenate(list(ind)), np.concatenate(list(ind1)))
 
         for i in range(len(results) - 1):
-            assert_allclose(
-                np.concatenate(list(results[i][0])),
-                np.concatenate(list(results[i + 1][0])),
-            ),
+            (
+                assert_allclose(
+                    np.concatenate(list(results[i][0])),
+                    np.concatenate(list(results[i + 1][0])),
+                ),
+            )
             assert_allclose(
                 np.concatenate(list(results[i][1])),
                 np.concatenate(list(results[i + 1][1])),

--- sklearn/tests/test_common.py
+++ sklearn/tests/test_common.py
@@ -345,7 +345,6 @@
     "transformer", GET_FEATURES_OUT_ESTIMATORS, ids=_get_check_estimator_ids
 )
 def test_transformers_get_feature_names_out(transformer):
-
     with ignore_warnings(category=(FutureWarning)):
         check_transformer_get_feature_names_out(
             transformer.__class__.__name__, transformer

--- sklearn/tests/test_metaestimators.py
+++ sklearn/tests/test_metaestimators.py
@@ -157,11 +157,12 @@
             if method in delegator_data.skip_methods:
                 continue
             assert hasattr(delegate, method)
-            assert hasattr(
-                delegator, method
-            ), "%s does not have method %r when its delegate does" % (
-                delegator_data.name,
-                method,
+            assert hasattr(delegator, method), (
+                "%s does not have method %r when its delegate does"
+                % (
+                    delegator_data.name,
+                    method,
+                )
             )
             # delegation before fit raises a NotFittedError
             if method == "score":
@@ -191,11 +192,12 @@
             delegate = SubEstimator(hidden_method=method)
             delegator = delegator_data.construct(delegate)
             assert not hasattr(delegate, method)
-            assert not hasattr(
-                delegator, method
-            ), "%s has method %r when its delegate does not" % (
-                delegator_data.name,
-                method,
+            assert not hasattr(delegator, method), (
+                "%s has method %r when its delegate does not"
+                % (
+                    delegator_data.name,
+                    method,
+                )
             )
 
 

--- sklearn/utils/_metadata_requests.py
+++ sklearn/utils/_metadata_requests.py
@@ -1098,8 +1098,9 @@
             method_mapping = MethodMapping()
             for method in METHODS:
                 method_mapping.add(caller=method, callee=method)
-            yield "$self_request", RouterMappingPair(
-                mapping=method_mapping, router=self._self_request
+            yield (
+                "$self_request",
+                RouterMappingPair(mapping=method_mapping, router=self._self_request),
             )
         for name, route_mapping in self._route_mappings.items():
             yield (name, route_mapping)

--- sklearn/utils/estimator_checks.py
+++ sklearn/utils/estimator_checks.py
@@ -4783,9 +4783,7 @@
                 'Only binary classification is supported. The type of the target '
                 f'is {{y_type}}.'
         )
-    """.format(
-        name=name
-    )
+    """.format(name=name)
     err_msg = textwrap.dedent(err_msg)
 
     with raises(

--- sklearn/utils/tests/test_multiclass.py
+++ sklearn/utils/tests/test_multiclass.py
@@ -416,12 +416,13 @@
 def test_type_of_target():
     for group, group_examples in EXAMPLES.items():
         for example in group_examples:
-            assert (
-                type_of_target(example) == group
-            ), "type_of_target(%r) should be %r, got %r" % (
-                example,
-                group,
-                type_of_target(example),
+            assert type_of_target(example) == group, (
+                "type_of_target(%r) should be %r, got %r"
+                % (
+                    example,
+                    group,
+                    type_of_target(example),
+                )
             )
 
     for example in NON_ARRAY_LIKE_EXAMPLES:

--- sklearn/utils/tests/test_seq_dataset.py
+++ sklearn/utils/tests/test_seq_dataset.py
@@ -154,30 +154,34 @@
 
 def test_buffer_dtype_mismatch_error():
     with pytest.raises(ValueError, match="Buffer dtype mismatch"):
-        ArrayDataset64(X32, y32, sample_weight32, seed=42),
+        (ArrayDataset64(X32, y32, sample_weight32, seed=42),)
 
     with pytest.raises(ValueError, match="Buffer dtype mismatch"):
-        ArrayDataset32(X64, y64, sample_weight64, seed=42),
+        (ArrayDataset32(X64, y64, sample_weight64, seed=42),)
 
     for csr_container in CSR_CONTAINERS:
         X_csr32 = csr_container(X32)
         X_csr64 = csr_container(X64)
         with pytest.raises(ValueError, match="Buffer dtype mismatch"):
-            CSRDataset64(
-                X_csr32.data,
-                X_csr32.indptr,
-                X_csr32.indices,
-                y32,
-                sample_weight32,
-                seed=42,
-            ),
+            (
+                CSRDataset64(
+                    X_csr32.data,
+                    X_csr32.indptr,
+                    X_csr32.indices,
+                    y32,
+                    sample_weight32,
+                    seed=42,
+                ),
+            )
 
         with pytest.raises(ValueError, match="Buffer dtype mismatch"):
-            CSRDataset32(
-                X_csr64.data,
-                X_csr64.indptr,
-                X_csr64.indices,
-                y64,
-                sample_weight64,
-                seed=42,
-            ),
+            (
+                CSRDataset32(
+                    X_csr64.data,
+                    X_csr64.indptr,
+                    X_csr64.indices,
+                    y64,
+                    sample_weight64,
+                    seed=42,
+                ),
+            )

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