deploy: braindecode/braindecode@f4e4493

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diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/2b3354037141fa0033174f982871d566/plot_relative_positioning.zip b/dev/_downloads/2b3354037141fa0033174f982871d566/plot_relative_positioning.zip
diff --git a/dev/_downloads/36393305d195c3207520572a3b677a05/plot_basic_training_epochs.zip b/dev/_downloads/36393305d195c3207520572a3b677a05/plot_basic_training_epochs.zip
diff --git a/dev/_downloads/3d616473194ad5c360edcbb3e46c7a3a/plot_custom_dataset_example.zip b/dev/_downloads/3d616473194ad5c360edcbb3e46c7a3a/plot_custom_dataset_example.zip
diff --git a/dev/_downloads/497b39027218ebaf8ffa20c07a0eff8d/plot_split_dataset.zip b/dev/_downloads/497b39027218ebaf8ffa20c07a0eff8d/plot_split_dataset.zip
diff --git a/dev/_downloads/4a2751c5805e7a17be8ee78ee1f2d520/plot_sleep_staging_chambon2018.zip b/dev/_downloads/4a2751c5805e7a17be8ee78ee1f2d520/plot_sleep_staging_chambon2018.zip
diff --git a/dev/_downloads/52f5b6b37eb9b07c97027b1eb0fadcd4/plot_sleep_staging_usleep.zip b/dev/_downloads/52f5b6b37eb9b07c97027b1eb0fadcd4/plot_sleep_staging_usleep.zip
diff --git a/dev/_downloads/568f264ca0ecc135d137fd575c39cf8e/plot_regression.zip b/dev/_downloads/568f264ca0ecc135d137fd575c39cf8e/plot_regression.zip
diff --git a/...ads/5e85dd7b06a544f298bd1837eddfcbd5/plot_train_in_pure_pytorch_and_pytorch_lightning.zip b/...ads/5e85dd7b06a544f298bd1837eddfcbd5/plot_train_in_pure_pytorch_and_pytorch_lightning.zip
diff --git a/dev/_downloads/67cf91b203bc10a20a2f8703a04849f8/plot_data_augmentation_search.zip b/dev/_downloads/67cf91b203bc10a20a2f8703a04849f8/plot_data_augmentation_search.zip
diff --git a/dev/_downloads/6b30e1d8632bb747aae46f30c6993983/plot_bcic_iv_4_ecog_cropped.zip b/dev/_downloads/6b30e1d8632bb747aae46f30c6993983/plot_bcic_iv_4_ecog_cropped.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/dev/_downloads/701321eb74424104536be933ed2cbf9e/plot_moabb_dataset_example.zip b/dev/_downloads/701321eb74424104536be933ed2cbf9e/plot_moabb_dataset_example.zip
diff --git a/dev/_downloads/7ec8b4809f7c475073a9b5d8ebb962dc/plot_benchmark_preprocessing.zip b/dev/_downloads/7ec8b4809f7c475073a9b5d8ebb962dc/plot_benchmark_preprocessing.zip
diff --git a/dev/_downloads/975f746daaf2fbe1dfde86e12f8d5031/plot_how_train_test_and_tune.zip b/dev/_downloads/975f746daaf2fbe1dfde86e12f8d5031/plot_how_train_test_and_tune.zip
diff --git a/dev/_downloads/9f417c6133a57e14b27122d013590718/plot_mne_dataset_example.zip b/dev/_downloads/9f417c6133a57e14b27122d013590718/plot_mne_dataset_example.zip
diff --git a/dev/_downloads/add1a85fee267a1849d27de66d7d8a6b/plot_sleep_staging_eldele2021.zip b/dev/_downloads/add1a85fee267a1849d27de66d7d8a6b/plot_sleep_staging_eldele2021.zip
diff --git a/dev/_downloads/b5c94eb60270f5ff2f26d4d743e7c69d/plot_tuh_eeg_corpus.zip b/dev/_downloads/b5c94eb60270f5ff2f26d4d743e7c69d/plot_tuh_eeg_corpus.zip
diff --git a/dev/_downloads/c3317f5b839dfe85c3fa27a60420173e/plot_bcic_iv_2a_moabb_cropped.zip b/dev/_downloads/c3317f5b839dfe85c3fa27a60420173e/plot_bcic_iv_2a_moabb_cropped.zip
diff --git a/dev/_downloads/c58c35b5588d025b3e00c7e383793f93/benchmark_lazy_eager_loading.zip b/dev/_downloads/c58c35b5588d025b3e00c7e383793f93/benchmark_lazy_eager_loading.zip
diff --git a/dev/_downloads/cec1d60200f666e0fada2d86280a2abe/plot_tuh_discrete_multitarget.zip b/dev/_downloads/cec1d60200f666e0fada2d86280a2abe/plot_tuh_discrete_multitarget.zip
diff --git a/dev/_downloads/d323b5e4b5903c1466d0e7674a0bfab6/plot_bcic_iv_2a_moabb_trial.zip b/dev/_downloads/d323b5e4b5903c1466d0e7674a0bfab6/plot_bcic_iv_2a_moabb_trial.zip
diff --git a/dev/_downloads/e824ae71d652d80edc1e0be50f3fcab4/plot_data_augmentation.zip b/dev/_downloads/e824ae71d652d80edc1e0be50f3fcab4/plot_data_augmentation.zip
diff --git a/...wnloads/e920a22508db3ee26e946faad811f57f/plot_hyperparameter_tuning_with_scikit-learn.zip b/...wnloads/e920a22508db3ee26e946faad811f57f/plot_hyperparameter_tuning_with_scikit-learn.zip
diff --git a/dev/_downloads/f3c7c35609f54876319e72ff42b71bb9/plot_load_save_datasets.zip b/dev/_downloads/f3c7c35609f54876319e72ff42b71bb9/plot_load_save_datasets.zip
diff --git a/dev/_downloads/f56b29a9031a522f5bcdaa29212f6171/plot_bcic_iv_4_ecog_trial.zip b/dev/_downloads/f56b29a9031a522f5bcdaa29212f6171/plot_bcic_iv_4_ecog_trial.zip
diff --git a/dev/_images/sphx_glr_plot_benchmark_preprocessing_001.png b/dev/_images/sphx_glr_plot_benchmark_preprocessing_001.png
diff --git a/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png b/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png
diff --git a/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html b/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html
@@ -605,7 +605,7 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this
 <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;crop&quot;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f1b519a1450&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f6961eb2500&gt;
 </pre></div>
 </div>
 <p>In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -842,14 +842,14 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
 -------  ----------  ----------  ------------  ------------  ------  ------
-      1    -23.7826     -4.6087        1.8225       11.7419  0.0006  0.5175
-      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4728
-      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4460
-      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4744
-      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4460
-      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4455
-      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4626
-      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4457
+      1    -23.7826     -4.6087        1.8225       11.7419  0.0006  0.5271
+      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4678
+      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4534
+      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4724
+      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4513
+      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4624
+      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4509
+      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4514
 </pre></div>
 </div>
 <p>Obtaining predictions and targets for the test, train, and validation dataset</p>
@@ -970,8 +970,8 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Link to this h
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 3.138 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1466 MB</p>
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (2 minutes 53.498 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  1381 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_4_ecog_cropped.ipynb</span></code></a></p>

diff --git a/dev/auto_examples/advanced_training/plot_data_augmentation.html b/dev/auto_examples/advanced_training/plot_data_augmentation.html
@@ -599,7 +599,7 @@ <h3><a class="toc-backref" href="#id3" role="doc-backlink">Preprocessing</a><a c
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:69: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&quot;Preprocessing choices with lambda functions cannot be saved.&quot;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f1c15560d30&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f68947b2920&gt;
 </pre></div>
 </div>
 </section>
@@ -793,10 +793,10 @@ <h3><a class="toc-backref" href="#id10" role="doc-backlink">Create an EEGClassif
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
 -------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
-      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.7435
-      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6014
-      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6057
-      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.6054
+      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.7984
+      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6008
+      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6195
+      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.6158
 
 &lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
   module_=============================================================================================================================================
@@ -850,8 +850,8 @@ <h3><a class="toc-backref" href="#id12" role="doc-backlink">Setting the data aug
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">composed_transforms</span></a>
 </pre></div>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.541 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  967 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 18.402 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  1129 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_data_augmentation.ipynb</span></code></a></p>

diff --git a/dev/auto_examples/advanced_training/plot_data_augmentation_search.html b/dev/auto_examples/advanced_training/plot_data_augmentation_search.html
@@ -620,7 +620,7 @@ <h3><a class="toc-backref" href="#id10" role="doc-backlink">Preprocessing</a><a
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:69: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&quot;Preprocessing choices with lambda functions cannot be saved.&quot;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f1c1504a380&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f6887348250&gt;
 </pre></div>
 </div>
 </section>
@@ -1389,8 +1389,8 @@ <h3><a class="toc-backref" href="#id19" role="doc-backlink">References</a><a cla
 Journal of Neural Engineering, 18(4), 046020.</p>
 </aside>
 </aside>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 32.930 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  729 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 33.960 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  749 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-search-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_data_augmentation_search.ipynb</span></code></a></p>