⚠️ CI failed on Linux_Docker.debian_atlas_32bit (last failure: Sep 03, 2024) ⚠️

**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=69809&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Sep 03, 2024)
- test_spectral_clustering_with_arpack_amg_solvers
- test_img_to_graph
- test_imputation_mean_median[csc_matrix]
- test_imputation_mean_median[csc_array]
- test_robust_scale_axis1

COMMENT:
Yet another one `test_regression_dataset` `global_random_seed=9`

```
[31m[1m________________ test_regression_dataset[9-0.5-absolute_error] _________________[0m

loss = 'absolute_error', subsample = 0.5, global_random_seed = 9

    [37m@pytest[39;49;00m.mark.parametrize([33m"[39;49;00m[33mloss[39;49;00m[33m"[39;49;00m, ([33m"[39;49;00m[33msquared_error[39;49;00m[33m"[39;49;00m, [33m"[39;49;00m[33mabsolute_error[39;49;00m[33m"[39;49;00m, [33m"[39;49;00m[33mhuber[39;49;00m[33m"[39;49;00m))
    [37m@pytest[39;49;00m.mark.parametrize([33m"[39;49;00m[33msubsample[39;49;00m[33m"[39;49;00m, ([94m1.0[39;49;00m, [94m0.5[39;49;00m))
    [94mdef[39;49;00m [92mtest_regression_dataset[39;49;00m(loss, subsample, global_random_seed):
        [90m# Check consistency on regression dataset with least squares[39;49;00m
        [90m# and least absolute deviation.[39;49;00m
        ones = np.ones([96mlen[39;49;00m(y_reg))
        last_y_pred = [94mNone[39;49;00m
        [94mfor[39;49;00m sample_weight [95min[39;49;00m [[94mNone[39;49;00m, ones, [94m2[39;49;00m * ones]:
            [90m# learning_rate, max_depth and n_estimators were adjusted to get a mode[39;49;00m
            [90m# that is accurate enough to reach a low MSE on the training set while[39;49;00m
            [90m# keeping the resource used to execute this test low enough.[39;49;00m
            reg = GradientBoostingRegressor(
                n_estimators=[94m30[39;49;00m,
                loss=loss,
                max_depth=[94m4[39;49;00m,
                subsample=subsample,
                min_samples_split=[94m2[39;49;00m,
                random_state=global_random_seed,
                learning_rate=[94m0.5[39;49;00m,
            )
    
            reg.fit(X_reg, y_reg, sample_weight=sample_weight)
            leaves = reg.apply(X_reg)
            [94massert[39;49;00m leaves.shape == ([94m100[39;49;00m, [94m30[39;49;00m)
    
            y_pred = reg.predict(X_reg)
            mse = mean_squared_error(y_reg, y_pred)
>           [94massert[39;49;00m mse < [94m0.05[39;49;00m
[1m[31mE           assert 0.061502134763447056 < 0.05[0m

global_random_seed = 9
last_y_pred = array([ 1.96914425e-01,  1.14397505e+00,  1.03623363e+00, -9.55692072e-01,
        1.28690942e+00, -1.20442879e+00, -3...0871e-01, -7.64560699e-01, -5.72143526e-02,
       -1.37658644e+00, -1.18460104e+00,  3.49999193e-01,  1.03889155e+00])
leaves     = array([[ 6.,  2., 14., ..., 11., 15.,  2.],
       [12., 12., 14., ..., 18.,  7., 14.],
       [12.,  7., 14., ..., 11... 2.,  8., ..., 16.,  4.,  6.],
       [12., 11., 11., ...,  6.,  7., 14.],
       [12., 12., 11., ..., 16.,  7., 13.]])
loss       = 'absolute_error'
mse        = 0.061502134763447056
ones       = array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., ...1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
reg        = GradientBoostingRegressor(learning_rate=0.5, loss='absolute_error', max_depth=4,
                          n_estimators=30, random_state=9, subsample=0.5)
sample_weight = array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., ...1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
subsample  = 0.5
y_pred     = array([ 0.70059485,  1.12371977,  1.19378893, -0.71641662,  1.30053896,
       -1.55576776, -0.35896728,  0.6208227 , ...940539,  1.00562572,  0.44547937, -0.70495051,
        0.02715904, -1.17364417, -1.1968037 ,  0.29633824,  1.22354559])

[1m[31m/io/sklearn/ensemble/tests/test_gradient_boosting.py[0m:172: AssertionError
----------------- generated xml file: /temp_dir/test-data.xml ------------------

```

Labels: Build / CI, Numerical Stability