deploy: braindecode/braindecode@65eb9ba

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diff --git a/dev/.buildinfo b/dev/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: dfa3b204b4ec02ee979e4bf266ee2b9c
+config: 80ca7f735dfcccb8968a28ca7e645b35
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
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diff --git a/dev/_images/sphx_glr_plot_sleep_staging_usleep_thumb.png b/dev/_images/sphx_glr_plot_sleep_staging_usleep_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
@@ -598,7 +598,7 @@ <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="s1">&#39;crop&#39;</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>
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-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4542b6bbb0&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4b8149c400&gt;
 </pre></div>
 </div>
 <p>In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -823,14 +823,14 @@ <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    -16.9037     -8.3086        2.5378       20.7324  0.0006  0.4844
-      2    -13.7539     -7.1244        1.9596       18.0720  0.0006  0.4082
-      3    -12.7768     -6.7402        1.6787       17.1870  0.0005  0.4264
-      4    -11.7970     -6.3910        1.5638       16.4409  0.0004  0.4111
-      5    -11.2712     -6.1840        1.4237       16.0095  0.0002  0.4141
-      6    -10.5735     -5.9203        1.3549       15.4436  0.0001  0.4042
-      7     -9.8228     -5.6181        1.3132       14.7799  0.0000  0.4198
-      8     -9.0643     -5.3047        1.3316       14.0884  0.0000  0.4033
+      1    -16.9036     -8.3098        2.5378       20.7346  0.0006  1.0870
+      2    -13.7536     -7.1257        1.9596       18.0738  0.0006  0.5969
+      3    -12.7771     -6.7413        1.6787       17.1884  0.0005  0.5724
+      4    -11.7976     -6.3917        1.5638       16.4413  0.0004  0.6026
+      5    -11.2720     -6.1845        1.4237       16.0095  0.0002  0.6533
+      6    -10.5744     -5.9208        1.3549       15.4433  0.0001  0.5924
+      7     -9.8236     -5.6185        1.3132       14.7797  0.0000  0.6048
+      8     -9.0650     -5.3051        1.3316       14.0882  0.0000  0.6093
 </pre></div>
 </div>
 <p>Obtaining predictions and targets for the test, train, and validation dataset</p>
@@ -940,8 +940,8 @@ <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 24.674 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  656 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> (1 minutes 55.097 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  810 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/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_4_ecog_cropped.py</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
@@ -589,7 +589,7 @@ <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:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f45423c4c10&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4b71698940&gt;
 </pre></div>
 </div>
 </section>
@@ -784,10 +784,10 @@ <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            0.2500        1.6077       0.2500            0.2500        3.0179  0.0006  1.7356
-      2            0.2569        1.3809       0.2569            0.2569        2.1898  0.0005  1.5168
-      3            0.3021        1.2767       0.2604            0.2604        1.7960  0.0002  1.5961
-      4            0.3229        1.1763       0.2674            0.2674        1.6417  0.0000  1.5376
+      1            0.2500        1.6077       0.2500            0.2500        3.0178  0.0006  2.7489
+      2            0.2569        1.3809       0.2569            0.2569        2.1896  0.0005  2.3996
+      3            0.3021        1.2767       0.2569            0.2569        1.7959  0.0002  2.3702
+      4            0.3229        1.1763       0.2674            0.2674        1.6416  0.0000  2.3995
 
 &lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
   module_=============================================================================================================================================
@@ -841,8 +841,8 @@ <h3><a class="toc-backref" href="#id12" role="doc-backlink">Setting the data aug
 <div class="highlight-default 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><strong>Estimated memory usage:</strong>  506 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 21.750 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  468 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-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_data_augmentation.py</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
@@ -610,7 +610,7 @@ <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:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f45416e9210&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4b714d6410&gt;
 </pre></div>
 </div>
 </section>
@@ -960,8 +960,8 @@ <h3><a class="toc-backref" href="#id19" role="doc-backlink">References</a><a cla
 Journal of Neural Engineering, 18(4), 046020.</p>
 </aside>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 31.872 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  522 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 51.725 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  106 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">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_data_augmentation_search.py</span></code></a></p>