diff --git a/docs/api/python/auto_examples/plot_backend.html b/docs/api/python/auto_examples/plot_backend.html
index b2318a5937..7ef17f6000 100644
--- a/docs/api/python/auto_examples/plot_backend.html
+++ b/docs/api/python/auto_examples/plot_backend.html
@@ -112,7 +112,7 @@ without using <em>onnx</em>.</p>
 <p>The backend API is implemented by other frameworks
 and makes it easier to switch between multiple runtimes
 with the same API.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.016 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.014 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-backend-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../downloads/3e23fa9ebb26f4728ee8426ed7da0f63/plot_backend.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_backend.py</span></code></a></p>
diff --git a/docs/api/python/auto_examples/plot_convert_pipeline_vectorizer.html b/docs/api/python/auto_examples/plot_convert_pipeline_vectorizer.html
index f6af62e477..4f48d64dd7 100644
--- a/docs/api/python/auto_examples/plot_convert_pipeline_vectorizer.html
+++ b/docs/api/python/auto_examples/plot_convert_pipeline_vectorizer.html
@@ -143,7 +143,7 @@ and we show the confusion matrix.</p>
 <span class="nb">print</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.9358557991605122
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.8778842817557302
 </pre></div>
 </div>
 </section>
@@ -200,12 +200,12 @@ ONNX Runtime expects one observation at a time.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">pred_onx</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.9999999999999337
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.999999999999964
 </pre></div>
 </div>
 <p>Very similar. <em>ONNX Runtime</em> uses floats instead of doubles,
 that explains the small discrepencies.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.066 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.044 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-convert-pipeline-vectorizer-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../downloads/982a1f7abbb8ffc5d5e98b671c35e5aa/plot_convert_pipeline_vectorizer.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_convert_pipeline_vectorizer.py</span></code></a></p>
diff --git a/docs/api/python/auto_examples/plot_load_and_predict.html b/docs/api/python/auto_examples/plot_load_and_predict.html
index fa86bd4c08..9110cb14a4 100644
--- a/docs/api/python/auto_examples/plot_load_and_predict.html
+++ b/docs/api/python/auto_examples/plot_load_and_predict.html
@@ -108,24 +108,24 @@ output type tensor(float)
 <span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[array([[[0.5179582 , 0.61270964, 0.61406195, 0.5725736 , 0.51330644],
-        [0.69485235, 0.5532911 , 0.52013624, 0.636309  , 0.70414793],
-        [0.6428818 , 0.63155353, 0.6381408 , 0.5325222 , 0.63684714],
-        [0.71618   , 0.54053146, 0.7182288 , 0.54457587, 0.67911494]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[array([[[0.7074605 , 0.66807246, 0.5468252 , 0.6794102 , 0.72581375],
+        [0.7061233 , 0.7108102 , 0.7131539 , 0.5087233 , 0.7157812 ],
+        [0.5101798 , 0.6822957 , 0.71132684, 0.63517916, 0.5935693 ],
+        [0.6674769 , 0.71915364, 0.6055379 , 0.6265797 , 0.6334329 ]],
 
-       [[0.54977566, 0.69168943, 0.56264675, 0.7119333 , 0.6331944 ],
-        [0.6970906 , 0.6601206 , 0.6880265 , 0.50476724, 0.71472955],
-        [0.72150964, 0.7020236 , 0.65242076, 0.58242726, 0.5292552 ],
-        [0.5488381 , 0.5718334 , 0.5828182 , 0.68686056, 0.7161767 ]],
+       [[0.7060813 , 0.65122193, 0.5852989 , 0.7020965 , 0.5418902 ],
+        [0.70865536, 0.7239054 , 0.53950447, 0.6397851 , 0.61991036],
+        [0.5108298 , 0.70998025, 0.5768114 , 0.70231366, 0.7083629 ],
+        [0.5532729 , 0.6634668 , 0.68702626, 0.53754365, 0.63848865]],
 
-       [[0.6186368 , 0.5709658 , 0.59708256, 0.627766  , 0.64522445],
-        [0.6574453 , 0.63655937, 0.6999107 , 0.70128393, 0.65033275],
-        [0.5789951 , 0.61559284, 0.66066134, 0.6682638 , 0.69809294],
-        [0.697786  , 0.6425333 , 0.6025326 , 0.6508598 , 0.62747025]]],
+       [[0.5546355 , 0.5326628 , 0.6045945 , 0.72216797, 0.5367474 ],
+        [0.54598904, 0.683926  , 0.7086522 , 0.5805207 , 0.6906233 ],
+        [0.71620524, 0.6052958 , 0.7310034 , 0.6245417 , 0.6648243 ],
+        [0.6229709 , 0.5226055 , 0.67183244, 0.6684347 , 0.57293963]]],
       dtype=float32)]
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.009 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.007 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-load-and-predict-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../downloads/7c8424f45d0156abd4d0221c65601124/plot_load_and_predict.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_load_and_predict.py</span></code></a></p>
diff --git a/docs/api/python/auto_examples/plot_pipeline.html b/docs/api/python/auto_examples/plot_pipeline.html
index 27473b680d..7eaed0f2a1 100644
--- a/docs/api/python/auto_examples/plot_pipeline.html
+++ b/docs/api/python/auto_examples/plot_pipeline.html
@@ -183,10 +183,10 @@ in a different way than before.</p>
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
 </pre></div>
 </div>
-<img src="../images/sphx_glr_plot_pipeline_001.png" srcset="../images/sphx_glr_plot_pipeline_001.png" alt="plot pipeline" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.image.AxesImage object at 0x7effb65e3d90&gt;
+<img src="../images/sphx_glr_plot_pipeline_001.png" srcset="../images/sphx_glr_plot_pipeline_001.png" alt="plot pipeline" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.image.AxesImage object at 0x7f0fecf2bd90&gt;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.221 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.136 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-pipeline-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../downloads/d436e9922b51a71358604ec00f09e7e4/plot_pipeline.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_pipeline.py</span></code></a></p>
diff --git a/docs/api/python/auto_examples/plot_profiling.html b/docs/api/python/auto_examples/plot_profiling.html
index f9a7675b12..16a2eb4441 100644
--- a/docs/api/python/auto_examples/plot_profiling.html
+++ b/docs/api/python/auto_examples/plot_profiling.html
@@ -106,7 +106,7 @@ before running the predictions.</p>
 <span class="nb">print</span><span class="p">(</span><span class="n">prof_file</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>onnxruntime_profile__2023-01-25_17-47-38.json
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>onnxruntime_profile__2023-01-26_22-44-30.json
 </pre></div>
 </div>
 <p>The results are stored un a file in JSON format.
@@ -122,20 +122,20 @@ Let’s see what it contains.</p>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[{&#39;args&#39;: {},
   &#39;cat&#39;: &#39;Session&#39;,
-  &#39;dur&#39;: 62,
+  &#39;dur&#39;: 59,
   &#39;name&#39;: &#39;model_loading_array&#39;,
   &#39;ph&#39;: &#39;X&#39;,
-  &#39;pid&#39;: 2783,
-  &#39;tid&#39;: 2783,
+  &#39;pid&#39;: 2745,
+  &#39;tid&#39;: 2745,
   &#39;ts&#39;: 1},
  {&#39;args&#39;: {},
   &#39;cat&#39;: &#39;Session&#39;,
-  &#39;dur&#39;: 283,
+  &#39;dur&#39;: 308,
   &#39;name&#39;: &#39;session_initialization&#39;,
   &#39;ph&#39;: &#39;X&#39;,
-  &#39;pid&#39;: 2783,
-  &#39;tid&#39;: 2783,
-  &#39;ts&#39;: 76}]
+  &#39;pid&#39;: 2745,
+  &#39;tid&#39;: 2745,
+  &#39;ts&#39;: 73}]
 </pre></div>
 </div>
 <p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.005 seconds)</p>
diff --git a/docs/api/python/auto_examples/plot_train_convert_predict.html b/docs/api/python/auto_examples/plot_train_convert_predict.html
index ea36661e60..a570aa8aab 100644
--- a/docs/api/python/auto_examples/plot_train_convert_predict.html
+++ b/docs/api/python/auto_examples/plot_train_convert_predict.html
@@ -86,6 +86,16 @@ to its use in its converted from.</p>
 <span class="n">clr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 </pre></div>
 </div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+</pre></div>
+</div>
 <div class="output_subarea output_html rendered_html output_result">
 <style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" checked><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div>
 </div>
@@ -98,9 +108,9 @@ and we show the confusion matrix.</p>
 <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[12  0  0]
- [ 0 12  1]
- [ 0  0 13]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[14  0  0]
+ [ 0 11  0]
+ [ 0  1 12]]
 </pre></div>
 </div>
 </section>
@@ -142,9 +152,9 @@ output name=&#39;output_label&#39; and shape=[None]
 <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">pred_onx</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[12  0  0]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[14  0  0]
  [ 0 12  0]
- [ 0  0 14]]
+ [ 0  0 12]]
 </pre></div>
 </div>
 <p>The prediction are perfectly identical.</p>
@@ -159,9 +169,9 @@ scikit-learn.</p>
 <span class="nb">print</span><span class="p">(</span><span class="n">prob_sklearn</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[9.64397638e-01 3.56022281e-02 1.33661432e-07]
- [1.12861883e-02 7.36931324e-01 2.51782488e-01]
- [9.71882114e-01 2.81177168e-02 1.68768358e-07]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[1.90908960e-01 8.02874408e-01 6.21663156e-03]
+ [2.81698299e-02 9.12759445e-01 5.90707247e-02]
+ [9.67801591e-01 3.21983324e-02 7.64754321e-08]]
 </pre></div>
 </div>
 <p>And then with ONNX Runtime.
@@ -174,9 +184,9 @@ The probabilies appear to be</p>
 <span class="n">pprint</span><span class="o">.</span><span class="n">pprint</span><span class="p">(</span><span class="n">prob_rt</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">])</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[{0: 0.964397668838501, 1: 0.03560224175453186, 2: 1.3366144457904738e-07},
- {0: 0.01128618698567152, 1: 0.7369312644004822, 2: 0.25178253650665283},
- {0: 0.9718821048736572, 1: 0.028117714449763298, 2: 1.6876846586910688e-07}]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[{0: 0.19090914726257324, 1: 0.8028742074966431, 2: 0.0062166303396224976},
+ {0: 0.02816985361278057, 1: 0.9127594232559204, 2: 0.059070732444524765},
+ {0: 0.9678016304969788, 1: 0.03219832107424736, 2: 7.647536648391906e-08}]
 </pre></div>
 </div>
 <p>Let’s benchmark.</p>
@@ -200,11 +210,11 @@ The probabilies appear to be</p>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for clr.predict
-Average 4.45e-05 min=4.23e-05 max=5.86e-05
+Average 4.44e-05 min=4.24e-05 max=5.4e-05
 Execution time for ONNX Runtime
-Average 2.2e-05 min=2.14e-05 max=2.68e-05
+Average 2.21e-05 min=2.16e-05 max=2.71e-05
 
-2.1959864999132605e-05
+2.2087500000509407e-05
 </pre></div>
 </div>
 <p>Let’s benchmark a scenario similar to what a webservice
@@ -232,11 +242,11 @@ as opposed to a batch of prediction.</p>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for clr.predict
-Average 0.00412 min=0.00409 max=0.00419
+Average 0.00406 min=0.00404 max=0.00414
 Execution time for sess_predict
-Average 0.00102 min=0.00101 max=0.00105
+Average 0.00104 min=0.00102 max=0.00107
 
-0.0010201415149995795
+0.00103513229499967
 </pre></div>
 </div>
 <p>Let’s do the same for the probabilities.</p>
@@ -253,11 +263,11 @@ Average 0.00102 min=0.00101 max=0.00105
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for predict_proba
-Average 0.0062 min=0.00618 max=0.00622
+Average 0.0061 min=0.00608 max=0.00626
 Execution time for sess_predict_proba
 Average 0.00108 min=0.00107 max=0.0011
 
-0.0010763829249992797
+0.001081757285000009
 </pre></div>
 </div>
 <p>This second comparison is better as
@@ -295,11 +305,11 @@ in every case.</p>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for predict_proba
-Average 0.676 min=0.675 max=0.678
+Average 0.674 min=0.672 max=0.678
 Execution time for sess_predict_proba
-Average 0.00136 min=0.00132 max=0.00164
+Average 0.0013 min=0.00129 max=0.00133
 
-0.0013557706149995852
+0.001301839815000676
 </pre></div>
 </div>
 <p>Let’s see with different number of trees.</p>
@@ -334,40 +344,40 @@ Average 0.00136 min=0.00132 max=0.00164
 </pre></div>
 </div>
 <img src="../images/sphx_glr_plot_train_convert_predict_001.png" srcset="../images/sphx_glr_plot_train_convert_predict_001.png" alt="Speed comparison between scikit-learn and ONNX Runtime For a random forest on Iris dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>5
-Average 0.0492 min=0.0491 max=0.0493
-Average 0.00102 min=0.00101 max=0.00104
+Average 0.0491 min=0.049 max=0.0491
+Average 0.00103 min=0.00102 max=0.00105
 10
-Average 0.0825 min=0.0824 max=0.0827
-Average 0.00105 min=0.00104 max=0.00107
+Average 0.0823 min=0.0822 max=0.0824
+Average 0.00104 min=0.00102 max=0.00106
 15
-Average 0.116 min=0.116 max=0.116
-Average 0.00104 min=0.00103 max=0.00107
+Average 0.115 min=0.115 max=0.115
+Average 0.00103 min=0.00102 max=0.00106
 20
-Average 0.149 min=0.149 max=0.149
-Average 0.00106 min=0.00105 max=0.00108
+Average 0.148 min=0.148 max=0.148
+Average 0.00105 min=0.00104 max=0.00108
 25
-Average 0.182 min=0.182 max=0.184
-Average 0.00108 min=0.00107 max=0.0011
+Average 0.181 min=0.181 max=0.182
+Average 0.00107 min=0.00106 max=0.00109
 30
-Average 0.215 min=0.214 max=0.215
-Average 0.00108 min=0.00108 max=0.0011
+Average 0.214 min=0.214 max=0.214
+Average 0.00107 min=0.00106 max=0.00109
 35
-Average 0.248 min=0.247 max=0.248
-Average 0.0011 min=0.00108 max=0.00113
+Average 0.247 min=0.247 max=0.248
+Average 0.00109 min=0.00108 max=0.00111
 40
-Average 0.281 min=0.281 max=0.281
-Average 0.0011 min=0.00109 max=0.00113
+Average 0.28 min=0.279 max=0.28
+Average 0.00109 min=0.00108 max=0.00112
 45
-Average 0.314 min=0.314 max=0.314
-Average 0.0011 min=0.00109 max=0.00113
+Average 0.312 min=0.311 max=0.312
+Average 0.00112 min=0.00111 max=0.00115
 50
-Average 0.347 min=0.347 max=0.348
-Average 0.00113 min=0.00112 max=0.00116
+Average 0.345 min=0.344 max=0.345
+Average 0.00112 min=0.00111 max=0.00114
 
-&lt;matplotlib.legend.Legend object at 0x7eff9f4d4400&gt;
+&lt;matplotlib.legend.Legend object at 0x7f0fd5bd2a40&gt;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  8.827 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  8.150 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-train-convert-predict-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../downloads/c647c128e0cf2b3db04ce60b41ef1a14/plot_train_convert_predict.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_train_convert_predict.py</span></code></a></p>
diff --git a/docs/api/python/auto_examples/sg_execution_times.html b/docs/api/python/auto_examples/sg_execution_times.html
index 1667ed8dc5..32a822ea17 100644
--- a/docs/api/python/auto_examples/sg_execution_times.html
+++ b/docs/api/python/auto_examples/sg_execution_times.html
@@ -49,7 +49,7 @@
             
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">¶</a></h1>
-<p><strong>03:10.156</strong> total execution time for <strong>auto_examples</strong> files:</p>
+<p><strong>03:09.367</strong> total execution time for <strong>auto_examples</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 85%" />
@@ -58,29 +58,29 @@
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 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_train_convert_predict.html#sphx-glr-auto-examples-plot-train-convert-predict-py"><span class="std std-ref">Train, convert and predict with ONNX Runtime</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_train_convert_predict.py</span></code>)</p></td>
-<td><p>03:08.827</p></td>
+<td><p>03:08.150</p></td>
 <td><p>0.0 MB</p></td>
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-<td><p>00:01.066</p></td>
+<td><p>00:01.044</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_pipeline.html#sphx-glr-auto-examples-plot-pipeline-py"><span class="std std-ref">Draw a pipeline</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_pipeline.py</span></code>)</p></td>
-<td><p>00:00.221</p></td>
+<td><p>00:00.136</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_backend.html#sphx-glr-auto-examples-plot-backend-py"><span class="std std-ref">ONNX Runtime Backend for ONNX</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_backend.py</span></code>)</p></td>
-<td><p>00:00.016</p></td>
+<td><p>00:00.014</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_load_and_predict.html#sphx-glr-auto-examples-plot-load-and-predict-py"><span class="std std-ref">Load and predict with ONNX Runtime and a very simple model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_load_and_predict.py</span></code>)</p></td>
-<td><p>00:00.009</p></td>
-<td><p>0.0 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_common_errors.html#sphx-glr-auto-examples-plot-common-errors-py"><span class="std std-ref">Common errors with onnxruntime</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_common_errors.py</span></code>)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_common_errors.html#sphx-glr-auto-examples-plot-common-errors-py"><span class="std std-ref">Common errors with onnxruntime</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_common_errors.py</span></code>)</p></td>
 <td><p>00:00.008</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_load_and_predict.html#sphx-glr-auto-examples-plot-load-and-predict-py"><span class="std std-ref">Load and predict with ONNX Runtime and a very simple model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_load_and_predict.py</span></code>)</p></td>
+<td><p>00:00.007</p></td>
+<td><p>0.0 MB</p></td>
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 <tr class="row-odd"><td><p><a class="reference internal" href="plot_profiling.html#sphx-glr-auto-examples-plot-profiling-py"><span class="std std-ref">Profile the execution of a simple model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_profiling.py</span></code>)</p></td>
 <td><p>00:00.005</p></td>
 <td><p>0.0 MB</p></td>
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[[0, "onnxruntime.InferenceSession.get_provider_options"]], "get_providers() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.get_providers"]], "get_run_config_entry() (onnxruntime.runoptions method)": [[0, "onnxruntime.RunOptions.get_run_config_entry"]], "get_session_config_entry() (onnxruntime.sessionoptions method)": [[0, "onnxruntime.SessionOptions.get_session_config_entry"]], "get_session_options() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.get_session_options"]], "graph_description (onnxruntime.modelmetadata property)": [[0, "onnxruntime.ModelMetadata.graph_description"]], "graph_name (onnxruntime.modelmetadata property)": [[0, "onnxruntime.ModelMetadata.graph_name"]], "graph_optimization_level (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.graph_optimization_level"]], "has_value() (onnxruntime.ortvalue method)": [[0, "onnxruntime.OrtValue.has_value"]], "inter_op_num_threads (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.inter_op_num_threads"]], "intra_op_num_threads (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.intra_op_num_threads"]], "io_binding() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.io_binding"]], "is_compatible() (in module onnxruntime.backend)": [[0, "onnxruntime.backend.is_compatible"]], "is_sparse_tensor() (onnxruntime.ortvalue method)": [[0, "onnxruntime.OrtValue.is_sparse_tensor"]], "is_tensor() (onnxruntime.ortvalue method)": [[0, "onnxruntime.OrtValue.is_tensor"]], "is_tensor_sequence() (onnxruntime.ortvalue method)": [[0, "onnxruntime.OrtValue.is_tensor_sequence"]], "log_severity_level (onnxruntime.runoptions property)": [[0, "onnxruntime.RunOptions.log_severity_level"]], "log_severity_level (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.log_severity_level"]], "log_verbosity_level (onnxruntime.runoptions property)": [[0, "onnxruntime.RunOptions.log_verbosity_level"]], "log_verbosity_level (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.log_verbosity_level"]], "logid (onnxruntime.runoptions property)": [[0, "onnxruntime.RunOptions.logid"]], "logid (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.logid"]], "name (onnxruntime.nodearg property)": [[0, "onnxruntime.NodeArg.name"]], "numpy() (onnxruntime.ortvalue method)": [[0, "onnxruntime.OrtValue.numpy"]], "only_execute_path_to_fetches (onnxruntime.runoptions property)": [[0, "onnxruntime.RunOptions.only_execute_path_to_fetches"]], "optimized_model_filepath (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.optimized_model_filepath"]], "ort_value_from_sparse_tensor() (onnxruntime.ortvalue static method)": [[0, "onnxruntime.OrtValue.ort_value_from_sparse_tensor"]], "ortvalue_from_numpy() (onnxruntime.ortvalue static method)": [[0, "onnxruntime.OrtValue.ortvalue_from_numpy"]], "ortvalue_from_shape_and_type() (onnxruntime.ortvalue static method)": [[0, "onnxruntime.OrtValue.ortvalue_from_shape_and_type"]], "prepare() (in module onnxruntime.backend)": [[0, "onnxruntime.backend.prepare"]], "producer_name (onnxruntime.modelmetadata property)": [[0, "onnxruntime.ModelMetadata.producer_name"]], "profile_file_prefix (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.profile_file_prefix"]], "register_custom_ops_library() (onnxruntime.sessionoptions method)": [[0, "onnxruntime.SessionOptions.register_custom_ops_library"]], "run() (in module onnxruntime.backend)": [[0, "onnxruntime.backend.run"]], "run() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.run"]], "run_with_iobinding() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.run_with_iobinding"]], "run_with_ort_values() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.run_with_ort_values"]], "run_with_ortvaluevector() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.run_with_ortvaluevector"]], "set_providers() (onnxruntime.inferencesession method)": [[0, "onnxruntime.InferenceSession.set_providers"]], "shape (onnxruntime.nodearg property)": [[0, "onnxruntime.NodeArg.shape"]], "shape() (onnxruntime.ortvalue method)": [[0, "onnxruntime.OrtValue.shape"]], "sparse_coo_from_numpy() (onnxruntime.sparsetensor static method)": [[0, "onnxruntime.SparseTensor.sparse_coo_from_numpy"]], "sparse_csr_from_numpy() (onnxruntime.sparsetensor static method)": [[0, "onnxruntime.SparseTensor.sparse_csr_from_numpy"]], "supports_device() (in module onnxruntime.backend)": [[0, "onnxruntime.backend.supports_device"]], "synchronize_execution_providers (onnxruntime.runoptions property)": [[0, "onnxruntime.RunOptions.synchronize_execution_providers"]], "terminate (onnxruntime.runoptions property)": [[0, "onnxruntime.RunOptions.terminate"]], "to_cuda() (onnxruntime.sparsetensor method)": [[0, "onnxruntime.SparseTensor.to_cuda"]], "type (onnxruntime.nodearg property)": [[0, "onnxruntime.NodeArg.type"]], "update_inplace() (onnxruntime.ortvalue method)": [[0, "onnxruntime.OrtValue.update_inplace"]], "use_deterministic_compute (onnxruntime.sessionoptions property)": [[0, "onnxruntime.SessionOptions.use_deterministic_compute"]], "values() (onnxruntime.sparsetensor method)": [[0, "onnxruntime.SparseTensor.values"]], "version (onnxruntime.modelmetadata property)": [[0, "onnxruntime.ModelMetadata.version"]]}})
\ No newline at end of file
diff --git a/docs/api/python/sources/auto_examples/plot_backend.rst.txt b/docs/api/python/sources/auto_examples/plot_backend.rst.txt
index 88ed18835f..97b6165afc 100644
--- a/docs/api/python/sources/auto_examples/plot_backend.rst.txt
+++ b/docs/api/python/sources/auto_examples/plot_backend.rst.txt
@@ -129,7 +129,7 @@ with the same API.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.016 seconds)
+   **Total running time of the script:** ( 0 minutes  0.014 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_backend.py:
diff --git a/docs/api/python/sources/auto_examples/plot_convert_pipeline_vectorizer.rst.txt b/docs/api/python/sources/auto_examples/plot_convert_pipeline_vectorizer.rst.txt
index 50799c7368..f9650b99af 100644
--- a/docs/api/python/sources/auto_examples/plot_convert_pipeline_vectorizer.rst.txt
+++ b/docs/api/python/sources/auto_examples/plot_convert_pipeline_vectorizer.rst.txt
@@ -151,7 +151,7 @@ and we show the confusion matrix.
 
  .. code-block:: none
 
-    0.9358557991605122
+    0.8778842817557302
 
 
 
@@ -284,7 +284,7 @@ We compare them to the model's ones.
 
  .. code-block:: none
 
-    0.9999999999999337
+    0.999999999999964
 
 
 
@@ -297,7 +297,7 @@ that explains the small discrepencies.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.066 seconds)
+   **Total running time of the script:** ( 0 minutes  1.044 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_convert_pipeline_vectorizer.py:
diff --git a/docs/api/python/sources/auto_examples/plot_load_and_predict.rst.txt b/docs/api/python/sources/auto_examples/plot_load_and_predict.rst.txt
index 699ce63698..908e1c1915 100644
--- a/docs/api/python/sources/auto_examples/plot_load_and_predict.rst.txt
+++ b/docs/api/python/sources/auto_examples/plot_load_and_predict.rst.txt
@@ -149,20 +149,20 @@ Let's compute its outputs (or predictions if it is a machine learned model).
 
  .. code-block:: none
 
-    [array([[[0.5179582 , 0.61270964, 0.61406195, 0.5725736 , 0.51330644],
-            [0.69485235, 0.5532911 , 0.52013624, 0.636309  , 0.70414793],
-            [0.6428818 , 0.63155353, 0.6381408 , 0.5325222 , 0.63684714],
-            [0.71618   , 0.54053146, 0.7182288 , 0.54457587, 0.67911494]],
+    [array([[[0.7074605 , 0.66807246, 0.5468252 , 0.6794102 , 0.72581375],
+            [0.7061233 , 0.7108102 , 0.7131539 , 0.5087233 , 0.7157812 ],
+            [0.5101798 , 0.6822957 , 0.71132684, 0.63517916, 0.5935693 ],
+            [0.6674769 , 0.71915364, 0.6055379 , 0.6265797 , 0.6334329 ]],
 
-           [[0.54977566, 0.69168943, 0.56264675, 0.7119333 , 0.6331944 ],
-            [0.6970906 , 0.6601206 , 0.6880265 , 0.50476724, 0.71472955],
-            [0.72150964, 0.7020236 , 0.65242076, 0.58242726, 0.5292552 ],
-            [0.5488381 , 0.5718334 , 0.5828182 , 0.68686056, 0.7161767 ]],
+           [[0.7060813 , 0.65122193, 0.5852989 , 0.7020965 , 0.5418902 ],
+            [0.70865536, 0.7239054 , 0.53950447, 0.6397851 , 0.61991036],
+            [0.5108298 , 0.70998025, 0.5768114 , 0.70231366, 0.7083629 ],
+            [0.5532729 , 0.6634668 , 0.68702626, 0.53754365, 0.63848865]],
 
-           [[0.6186368 , 0.5709658 , 0.59708256, 0.627766  , 0.64522445],
-            [0.6574453 , 0.63655937, 0.6999107 , 0.70128393, 0.65033275],
-            [0.5789951 , 0.61559284, 0.66066134, 0.6682638 , 0.69809294],
-            [0.697786  , 0.6425333 , 0.6025326 , 0.6508598 , 0.62747025]]],
+           [[0.5546355 , 0.5326628 , 0.6045945 , 0.72216797, 0.5367474 ],
+            [0.54598904, 0.683926  , 0.7086522 , 0.5805207 , 0.6906233 ],
+            [0.71620524, 0.6052958 , 0.7310034 , 0.6245417 , 0.6648243 ],
+            [0.6229709 , 0.5226055 , 0.67183244, 0.6684347 , 0.57293963]]],
           dtype=float32)]
 
 
@@ -171,7 +171,7 @@ Let's compute its outputs (or predictions if it is a machine learned model).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.009 seconds)
+   **Total running time of the script:** ( 0 minutes  0.007 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_load_and_predict.py:
diff --git a/docs/api/python/sources/auto_examples/plot_pipeline.rst.txt b/docs/api/python/sources/auto_examples/plot_pipeline.rst.txt
index e0ab76ad18..7e62c4d7e0 100644
--- a/docs/api/python/sources/auto_examples/plot_pipeline.rst.txt
+++ b/docs/api/python/sources/auto_examples/plot_pipeline.rst.txt
@@ -226,14 +226,14 @@ Which we display...
  .. code-block:: none
 
 
-    <matplotlib.image.AxesImage object at 0x7effb65e3d90>
+    <matplotlib.image.AxesImage object at 0x7f0fecf2bd90>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.221 seconds)
+   **Total running time of the script:** ( 0 minutes  0.136 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_pipeline.py:
diff --git a/docs/api/python/sources/auto_examples/plot_profiling.rst.txt b/docs/api/python/sources/auto_examples/plot_profiling.rst.txt
index f686a8c19d..597b5fbede 100644
--- a/docs/api/python/sources/auto_examples/plot_profiling.rst.txt
+++ b/docs/api/python/sources/auto_examples/plot_profiling.rst.txt
@@ -117,7 +117,7 @@ before running the predictions.
 
  .. code-block:: none
 
-    onnxruntime_profile__2023-01-25_17-47-38.json
+    onnxruntime_profile__2023-01-26_22-44-30.json
 
 
 
@@ -148,20 +148,20 @@ Let's see what it contains.
 
     [{'args': {},
       'cat': 'Session',
-      'dur': 62,
+      'dur': 59,
       'name': 'model_loading_array',
       'ph': 'X',
-      'pid': 2783,
-      'tid': 2783,
+      'pid': 2745,
+      'tid': 2745,
       'ts': 1},
      {'args': {},
       'cat': 'Session',
-      'dur': 283,
+      'dur': 308,
       'name': 'session_initialization',
       'ph': 'X',
-      'pid': 2783,
-      'tid': 2783,
-      'ts': 76}]
+      'pid': 2745,
+      'tid': 2745,
+      'ts': 73}]
 
 
 
diff --git a/docs/api/python/sources/auto_examples/plot_train_convert_predict.rst.txt b/docs/api/python/sources/auto_examples/plot_train_convert_predict.rst.txt
index 61826b213e..42cb584afd 100644
--- a/docs/api/python/sources/auto_examples/plot_train_convert_predict.rst.txt
+++ b/docs/api/python/sources/auto_examples/plot_train_convert_predict.rst.txt
@@ -74,6 +74,19 @@ Then we fit a model.
 
 
 
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    /home/runner/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
+    STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+    Increase the number of iterations (max_iter) or scale the data as shown in:
+        https://scikit-learn.org/stable/modules/preprocessing.html
+    Please also refer to the documentation for alternative solver options:
+        https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+      n_iter_i = _check_optimize_result(
+
 
 .. raw:: html
 
@@ -105,9 +118,9 @@ and we show the confusion matrix.
 
  .. code-block:: none
 
-    [[12  0  0]
-     [ 0 12  1]
-     [ 0  0 13]]
+    [[14  0  0]
+     [ 0 11  0]
+     [ 0  1 12]]
 
 
 
@@ -197,9 +210,9 @@ We compute the predictions.
 
  .. code-block:: none
 
-    [[12  0  0]
+    [[14  0  0]
      [ 0 12  0]
-     [ 0  0 14]]
+     [ 0  0 12]]
 
 
 
@@ -232,9 +245,9 @@ scikit-learn.
 
  .. code-block:: none
 
-    [[9.64397638e-01 3.56022281e-02 1.33661432e-07]
-     [1.12861883e-02 7.36931324e-01 2.51782488e-01]
-     [9.71882114e-01 2.81177168e-02 1.68768358e-07]]
+    [[1.90908960e-01 8.02874408e-01 6.21663156e-03]
+     [2.81698299e-02 9.12759445e-01 5.90707247e-02]
+     [9.67801591e-01 3.21983324e-02 7.64754321e-08]]
 
 
 
@@ -264,9 +277,9 @@ The probabilies appear to be
 
  .. code-block:: none
 
-    [{0: 0.964397668838501, 1: 0.03560224175453186, 2: 1.3366144457904738e-07},
-     {0: 0.01128618698567152, 1: 0.7369312644004822, 2: 0.25178253650665283},
-     {0: 0.9718821048736572, 1: 0.028117714449763298, 2: 1.6876846586910688e-07}]
+    [{0: 0.19090914726257324, 1: 0.8028742074966431, 2: 0.0062166303396224976},
+     {0: 0.02816985361278057, 1: 0.9127594232559204, 2: 0.059070732444524765},
+     {0: 0.9678016304969788, 1: 0.03219832107424736, 2: 7.647536648391906e-08}]
 
 
 
@@ -306,11 +319,11 @@ Let's benchmark.
  .. code-block:: none
 
     Execution time for clr.predict
-    Average 4.45e-05 min=4.23e-05 max=5.86e-05
+    Average 4.44e-05 min=4.24e-05 max=5.4e-05
     Execution time for ONNX Runtime
-    Average 2.2e-05 min=2.14e-05 max=2.68e-05
+    Average 2.21e-05 min=2.16e-05 max=2.71e-05
 
-    2.1959864999132605e-05
+    2.2087500000509407e-05
 
 
 
@@ -355,11 +368,11 @@ as opposed to a batch of prediction.
  .. code-block:: none
 
     Execution time for clr.predict
-    Average 0.00412 min=0.00409 max=0.00419
+    Average 0.00406 min=0.00404 max=0.00414
     Execution time for sess_predict
-    Average 0.00102 min=0.00101 max=0.00105
+    Average 0.00104 min=0.00102 max=0.00107
 
-    0.0010201415149995795
+    0.00103513229499967
 
 
 
@@ -392,11 +405,11 @@ Let's do the same for the probabilities.
  .. code-block:: none
 
     Execution time for predict_proba
-    Average 0.0062 min=0.00618 max=0.00622
+    Average 0.0061 min=0.00608 max=0.00626
     Execution time for sess_predict_proba
     Average 0.00108 min=0.00107 max=0.0011
 
-    0.0010763829249992797
+    0.001081757285000009
 
 
 
@@ -466,11 +479,11 @@ We compare.
  .. code-block:: none
 
     Execution time for predict_proba
-    Average 0.676 min=0.675 max=0.678
+    Average 0.674 min=0.672 max=0.678
     Execution time for sess_predict_proba
-    Average 0.00136 min=0.00132 max=0.00164
+    Average 0.0013 min=0.00129 max=0.00133
 
-    0.0013557706149995852
+    0.001301839815000676
 
 
 
@@ -525,44 +538,44 @@ Let's see with different number of trees.
  .. code-block:: none
 
     5
-    Average 0.0492 min=0.0491 max=0.0493
-    Average 0.00102 min=0.00101 max=0.00104
+    Average 0.0491 min=0.049 max=0.0491
+    Average 0.00103 min=0.00102 max=0.00105
     10
-    Average 0.0825 min=0.0824 max=0.0827
-    Average 0.00105 min=0.00104 max=0.00107
+    Average 0.0823 min=0.0822 max=0.0824
+    Average 0.00104 min=0.00102 max=0.00106
     15
-    Average 0.116 min=0.116 max=0.116
-    Average 0.00104 min=0.00103 max=0.00107
+    Average 0.115 min=0.115 max=0.115
+    Average 0.00103 min=0.00102 max=0.00106
     20
-    Average 0.149 min=0.149 max=0.149
-    Average 0.00106 min=0.00105 max=0.00108
+    Average 0.148 min=0.148 max=0.148
+    Average 0.00105 min=0.00104 max=0.00108
     25
-    Average 0.182 min=0.182 max=0.184
-    Average 0.00108 min=0.00107 max=0.0011
+    Average 0.181 min=0.181 max=0.182
+    Average 0.00107 min=0.00106 max=0.00109
     30
-    Average 0.215 min=0.214 max=0.215
-    Average 0.00108 min=0.00108 max=0.0011
+    Average 0.214 min=0.214 max=0.214
+    Average 0.00107 min=0.00106 max=0.00109
     35
-    Average 0.248 min=0.247 max=0.248
-    Average 0.0011 min=0.00108 max=0.00113
+    Average 0.247 min=0.247 max=0.248
+    Average 0.00109 min=0.00108 max=0.00111
     40
-    Average 0.281 min=0.281 max=0.281
-    Average 0.0011 min=0.00109 max=0.00113
+    Average 0.28 min=0.279 max=0.28
+    Average 0.00109 min=0.00108 max=0.00112
     45
-    Average 0.314 min=0.314 max=0.314
-    Average 0.0011 min=0.00109 max=0.00113
+    Average 0.312 min=0.311 max=0.312
+    Average 0.00112 min=0.00111 max=0.00115
     50
-    Average 0.347 min=0.347 max=0.348
-    Average 0.00113 min=0.00112 max=0.00116
+    Average 0.345 min=0.344 max=0.345
+    Average 0.00112 min=0.00111 max=0.00114
 
-    <matplotlib.legend.Legend object at 0x7eff9f4d4400>
+    <matplotlib.legend.Legend object at 0x7f0fd5bd2a40>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 3 minutes  8.827 seconds)
+   **Total running time of the script:** ( 3 minutes  8.150 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_train_convert_predict.py:
diff --git a/docs/api/python/sources/auto_examples/sg_execution_times.rst.txt b/docs/api/python/sources/auto_examples/sg_execution_times.rst.txt
index 6345ee913c..e80acbe6ad 100644
--- a/docs/api/python/sources/auto_examples/sg_execution_times.rst.txt
+++ b/docs/api/python/sources/auto_examples/sg_execution_times.rst.txt
@@ -5,21 +5,21 @@
 
 Computation times
 =================
-**03:10.156** total execution time for **auto_examples** files:
+**03:09.367** total execution time for **auto_examples** files:
 
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_train_convert_predict.py` (``plot_train_convert_predict.py``)             | 03:08.827 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_train_convert_predict.py` (``plot_train_convert_predict.py``)             | 03:08.150 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_convert_pipeline_vectorizer.py` (``plot_convert_pipeline_vectorizer.py``) | 00:01.066 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_convert_pipeline_vectorizer.py` (``plot_convert_pipeline_vectorizer.py``) | 00:01.044 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_pipeline.py` (``plot_pipeline.py``)                                       | 00:00.221 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_pipeline.py` (``plot_pipeline.py``)                                       | 00:00.136 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_backend.py` (``plot_backend.py``)                                         | 00:00.016 | 0.0 MB |
-+-------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_load_and_predict.py` (``plot_load_and_predict.py``)                       | 00:00.009 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_backend.py` (``plot_backend.py``)                                         | 00:00.014 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_common_errors.py` (``plot_common_errors.py``)                             | 00:00.008 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_plot_load_and_predict.py` (``plot_load_and_predict.py``)                       | 00:00.007 | 0.0 MB |
++-------------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_profiling.py` (``plot_profiling.py``)                                     | 00:00.005 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_metadata.py` (``plot_metadata.py``)                                       | 00:00.003 | 0.0 MB |
diff --git a/docs/api/python/tutorial.html b/docs/api/python/tutorial.html
index dfd2de22db..17f20afbc0 100644
--- a/docs/api/python/tutorial.html
+++ b/docs/api/python/tutorial.html
@@ -85,15 +85,7 @@ ONNX format and run the first predictions.</p>
 </pre></div>
 </div>
 <p>&gt;&gt;&gt;</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">runner</span><span class="o">/.</span><span class="n">local</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="mf">.10</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">sklearn</span><span class="o">/</span><span class="n">linear_model</span><span class="o">/</span><span class="n">_logistic</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">444</span><span class="p">:</span> <span class="n">ConvergenceWarning</span><span class="p">:</span> <span class="n">lbfgs</span> <span class="n">failed</span> <span class="n">to</span> <span class="n">converge</span> <span class="p">(</span><span class="n">status</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-    <span class="n">STOP</span><span class="p">:</span> <span class="n">TOTAL</span> <span class="n">NO</span><span class="o">.</span> <span class="n">of</span> <span class="n">ITERATIONS</span> <span class="n">REACHED</span> <span class="n">LIMIT</span><span class="o">.</span>
-    
-    <span class="n">Increase</span> <span class="n">the</span> <span class="n">number</span> <span class="n">of</span> <span class="n">iterations</span> <span class="p">(</span><span class="n">max_iter</span><span class="p">)</span> <span class="ow">or</span> <span class="n">scale</span> <span class="n">the</span> <span class="n">data</span> <span class="k">as</span> <span class="n">shown</span> <span class="ow">in</span><span class="p">:</span>
-        <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="n">stable</span><span class="o">/</span><span class="n">modules</span><span class="o">/</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">html</span>
-    <span class="n">Please</span> <span class="n">also</span> <span class="n">refer</span> <span class="n">to</span> <span class="n">the</span> <span class="n">documentation</span> <span class="k">for</span> <span class="n">alternative</span> <span class="n">solver</span> <span class="n">options</span><span class="p">:</span>
-        <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="n">stable</span><span class="o">/</span><span class="n">modules</span><span class="o">/</span><span class="n">linear_model</span><span class="o">.</span><span class="n">html</span><span class="c1">#logistic-regression</span>
-      <span class="n">n_iter_i</span> <span class="o">=</span> <span class="n">_check_optimize_result</span><span class="p">(</span>
-    <span class="n">LogisticRegression</span><span class="p">()</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">LogisticRegression</span><span class="p">()</span>
 </pre></div>
 </div>
 </section>
@@ -136,8 +128,8 @@ for this machine learning model.</p>
 </pre></div>
 </div>
 <p>&gt;&gt;&gt;</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="p">[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span>
-     <span class="mi">2</span><span class="p">]</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="p">[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span>
+     <span class="mi">1</span><span class="p">]</span>
 </pre></div>
 </div>
 <p>The code can be changed to get one specific output
@@ -156,8 +148,8 @@ by specifying its name into a list.</p>
 </pre></div>
 </div>
 <p>&gt;&gt;&gt;</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="p">[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span>
-     <span class="mi">2</span><span class="p">]</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="p">[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">1</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span>
+     <span class="mi">1</span><span class="p">]</span>
 </pre></div>
 </div>
 </section>