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diff --git a/_sources/pages/mzi.ipynb b/_sources/pages/mzi.ipynb
index c0fca61..699df22 100644
--- a/_sources/pages/mzi.ipynb
+++ b/_sources/pages/mzi.ipynb
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +199,7 @@
       "-----\n",
       "See also the PDK documentation:\n",
       "https://github.com/SiEPIC/SiEPIC_EBeam_PDK/blob/master/Documentation/SiEPIC_EBeam_PDK%20-%20Components%20with%20Models.docx\n",
-      "\u001b[0;31mFile:\u001b[0m      ~/git/simphony/simphony/libraries/siepic/models.py\n",
+      "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/photonics/lib/python3.11/site-packages/simphony/libraries/siepic/models.py\n",
       "\u001b[0;31mType:\u001b[0m      function"
      ]
     }
@@ -395,34 +395,22 @@
     {
      "data": {
       "text/plain": [
-       "{'gc_in': {'wl': Array(1.55, dtype=float64),\n",
+       "{'gc_in': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'dwidth': 0},\n",
+       " 'splitter': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'width': 500.0},\n",
+       " 'long_wg': {'wl': 1.55,\n",
        "  'pol': 'te',\n",
-       "  'thickness': Array(220., dtype=float64),\n",
-       "  'dwidth': Array(0., dtype=float64)},\n",
-       " 'splitter': {'wl': Array(1.55, dtype=float64),\n",
+       "  'length': 0.0,\n",
+       "  'width': 500.0,\n",
+       "  'height': 220.0,\n",
+       "  'loss': 0.0},\n",
+       " 'short_wg': {'wl': 1.55,\n",
        "  'pol': 'te',\n",
-       "  'thickness': Array(220., dtype=float64),\n",
-       "  'width': Array(500., dtype=float64)},\n",
-       " 'long_wg': {'wl': Array(1.55, dtype=float64),\n",
-       "  'pol': 'te',\n",
-       "  'length': Array(0., dtype=float64),\n",
-       "  'width': Array(500., dtype=float64),\n",
-       "  'height': Array(220., dtype=float64),\n",
-       "  'loss': Array(0., dtype=float64)},\n",
-       " 'short_wg': {'wl': Array(1.55, dtype=float64),\n",
-       "  'pol': 'te',\n",
-       "  'length': Array(0., dtype=float64),\n",
-       "  'width': Array(500., dtype=float64),\n",
-       "  'height': Array(220., dtype=float64),\n",
-       "  'loss': Array(0., dtype=float64)},\n",
-       " 'combiner': {'wl': Array(1.55, dtype=float64),\n",
-       "  'pol': 'te',\n",
-       "  'thickness': Array(220., dtype=float64),\n",
-       "  'width': Array(500., dtype=float64)},\n",
-       " 'gc_out': {'wl': Array(1.55, dtype=float64),\n",
-       "  'pol': 'te',\n",
-       "  'thickness': Array(220., dtype=float64),\n",
-       "  'dwidth': Array(0., dtype=float64)}}"
+       "  'length': 0.0,\n",
+       "  'width': 500.0,\n",
+       "  'height': 220.0,\n",
+       "  'loss': 0.0},\n",
+       " 'combiner': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'width': 500.0},\n",
+       " 'gc_out': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'dwidth': 0}}"
       ]
      },
      "execution_count": 8,
@@ -516,22 +504,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-01-09 23:00:01.222\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m785\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/sequoia/git/Photonics-Bootcamp/env/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.\u001b[0m\n",
-      "\u001b[32m2024-01-09 23:00:01.227\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.pdk\u001b[0m:\u001b[36mactivate\u001b[0m:\u001b[36m258\u001b[0m - \u001b[1m'generic' PDK is now active\u001b[0m\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -541,14 +521,11 @@
    "source": [
     "import gdsfactory as gf\n",
     "\n",
-    "PDK = gf.get_generic_pdk()\n",
-    "PDK.activate()\n",
-    "\n",
     "# The << is shorthand for c.add_ref()\n",
     "c = gf.Component(\"my_component\")\n",
     "mzi = c << gf.components.mzi(delta_length=0)\n",
     "\n",
-    "c.plot_matplotlib()"
+    "c.plot()"
    ]
   },
   {
@@ -640,22 +617,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-01-09 22:59:50.499\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m785\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/sequoia/git/Photonics-Bootcamp/env/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.\u001b[0m\n",
-      "\u001b[32m2024-01-09 22:59:50.508\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.pdk\u001b[0m:\u001b[36mactivate\u001b[0m:\u001b[36m258\u001b[0m - \u001b[1m'generic' PDK is now active\u001b[0m\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -665,13 +634,10 @@
    "source": [
     "import gdsfactory as gf\n",
     "\n",
-    "PDK = gf.get_generic_pdk()\n",
-    "PDK.activate()\n",
-    "\n",
     "c = gf.Component(\"my_component\")\n",
     "mzi = c << gf.components.mzi(delta_length=15)\n",
     "\n",
-    "c.plot_matplotlib()"
+    "c.plot()"
    ]
   },
   {
@@ -744,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.7"
   },
   "orig_nbformat": 4
  },
diff --git a/pages/directional_couplers.html b/pages/directional_couplers.html
index cdd9e3c..9bce10b 100644
--- a/pages/directional_couplers.html
+++ b/pages/directional_couplers.html
@@ -564,8 +564,8 @@ <h3>Coupling length and the gap between waveguides<a class="headerlink" href="#c
 \]</div>
 <p>Because of this difference in propagation constants, the modes travel at different speeds down the waveguide and so the field intensity oscillates between the two waveguides. This is called beating. One beat is equivalent to the coupling length, or cross-over length, and it is the length it takes for all of the power from one waveguide to be coupled into the other. As the gap between the waveguides gets smaller, the beating gets faster since <span class="math notranslate nohighlight">\(\Delta n\)</span> gets larger, making the cross-over length shorter.</p>
 <p>This cross-over length <span class="math notranslate nohighlight">\(L\)</span>, that gives 100% power transfer is found with:</p>
-<div class="amsmath math notranslate nohighlight" id="equation-f02fdd01-2fb4-4716-a30c-4da9a44c10cb">
-<span class="eqno">(2)<a class="headerlink" href="#equation-f02fdd01-2fb4-4716-a30c-4da9a44c10cb" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-f70109e6-c7e2-43a2-9e64-fd60e1842c91">
+<span class="eqno">(2)<a class="headerlink" href="#equation-f70109e6-c7e2-43a2-9e64-fd60e1842c91" title="Permalink to this equation">#</a></span>\[\begin{align}
 L_{\text{cross-over}} = \frac {\lambda}{2\Delta n} \nonumber
 \end{align}\]</div>
 <p>This is found from determining what length when multiplied by the propagation constants makes the phase difference <span class="math notranslate nohighlight">\(\pi\)</span>:</p>
diff --git a/pages/mzi.html b/pages/mzi.html
index 27c52a2..20f7e8b 100644
--- a/pages/mzi.html
+++ b/pages/mzi.html
@@ -448,7 +448,7 @@ <h2>Models<a class="headerlink" href="#models" title="Permalink to this heading"
 -----
 See also the PDK documentation:
 https://github.com/SiEPIC/SiEPIC_EBeam_PDK/blob/master/Documentation/SiEPIC_EBeam_PDK%20-%20Components%20with%20Models.docx
-<span class="-Color -Color-Red">File:</span>      ~/git/simphony/simphony/libraries/siepic/models.py
+<span class="-Color -Color-Red">File:</span>      ~/miniconda3/envs/photonics/lib/python3.11/site-packages/simphony/libraries/siepic/models.py
 <span class="-Color -Color-Red">Type:</span>      function
 </pre></div>
 </div>
@@ -581,34 +581,22 @@ <h2>Simulation (using callables)<a class="headerlink" href="#simulation-using-ca
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{'gc_in': {'wl': Array(1.55, dtype=float64),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{'gc_in': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'dwidth': 0},
+ 'splitter': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'width': 500.0},
+ 'long_wg': {'wl': 1.55,
   'pol': 'te',
-  'thickness': Array(220., dtype=float64),
-  'dwidth': Array(0., dtype=float64)},
- 'splitter': {'wl': Array(1.55, dtype=float64),
+  'length': 0.0,
+  'width': 500.0,
+  'height': 220.0,
+  'loss': 0.0},
+ 'short_wg': {'wl': 1.55,
   'pol': 'te',
-  'thickness': Array(220., dtype=float64),
-  'width': Array(500., dtype=float64)},
- 'long_wg': {'wl': Array(1.55, dtype=float64),
-  'pol': 'te',
-  'length': Array(0., dtype=float64),
-  'width': Array(500., dtype=float64),
-  'height': Array(220., dtype=float64),
-  'loss': Array(0., dtype=float64)},
- 'short_wg': {'wl': Array(1.55, dtype=float64),
-  'pol': 'te',
-  'length': Array(0., dtype=float64),
-  'width': Array(500., dtype=float64),
-  'height': Array(220., dtype=float64),
-  'loss': Array(0., dtype=float64)},
- 'combiner': {'wl': Array(1.55, dtype=float64),
-  'pol': 'te',
-  'thickness': Array(220., dtype=float64),
-  'width': Array(500., dtype=float64)},
- 'gc_out': {'wl': Array(1.55, dtype=float64),
-  'pol': 'te',
-  'thickness': Array(220., dtype=float64),
-  'dwidth': Array(0., dtype=float64)}}
+  'length': 0.0,
+  'width': 500.0,
+  'height': 220.0,
+  'loss': 0.0},
+ 'combiner': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'width': 500.0},
+ 'gc_out': {'wl': 1.55, 'pol': 'te', 'thickness': 220.0, 'dwidth': 0}}
 </pre></div>
 </div>
 </div>
@@ -654,23 +642,16 @@ <h2>Analysis of a balanced MZI<a class="headerlink" href="#analysis-of-a-balance
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gdsfactory</span> <span class="k">as</span> <span class="nn">gf</span>
 
-<span class="n">PDK</span> <span class="o">=</span> <span class="n">gf</span><span class="o">.</span><span class="n">get_generic_pdk</span><span class="p">()</span>
-<span class="n">PDK</span><span class="o">.</span><span class="n">activate</span><span class="p">()</span>
-
 <span class="c1"># The &lt;&lt; is shorthand for c.add_ref()</span>
 <span class="n">c</span> <span class="o">=</span> <span class="n">gf</span><span class="o">.</span><span class="n">Component</span><span class="p">(</span><span class="s2">"my_component"</span><span class="p">)</span>
 <span class="n">mzi</span> <span class="o">=</span> <span class="n">c</span> <span class="o">&lt;&lt;</span> <span class="n">gf</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">mzi</span><span class="p">(</span><span class="n">delta_length</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
-<span class="n">c</span><span class="o">.</span><span class="n">plot_matplotlib</span><span class="p">()</span>
+<span class="n">c</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><span class="-Color -Color-Green">2024-01-09 23:00:01.222</span> | <span class="-Color -Color-Bold">INFO    </span> | <span class="-Color -Color-Cyan">gdsfactory.technology.layer_views</span>:<span class="-Color -Color-Cyan">__init__</span>:<span class="-Color -Color-Cyan">785</span> - <span class="-Color -Color-Bold">Importing LayerViews from YAML file: '/home/sequoia/git/Photonics-Bootcamp/env/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.</span>
-<span class="-Color -Color-Green">2024-01-09 23:00:01.227</span> | <span class="-Color -Color-Bold">INFO    </span> | <span class="-Color -Color-Cyan">gdsfactory.pdk</span>:<span class="-Color -Color-Cyan">activate</span>:<span class="-Color -Color-Cyan">258</span> - <span class="-Color -Color-Bold">'generic' PDK is now active</span>
-</pre></div>
-</div>
-<img alt="../_images/d30d68ddd43e49e0882b36f1269edec6dc7dbfabcadf0dd3dea898b99afe6fe2.png" src="../_images/d30d68ddd43e49e0882b36f1269edec6dc7dbfabcadf0dd3dea898b99afe6fe2.png"/>
+<img alt="../_images/6f55ba31bf0a482c807b04fc82cf7fc9711ae9df443b39ff96562fa47613df31.png" src="../_images/6f55ba31bf0a482c807b04fc82cf7fc9711ae9df443b39ff96562fa47613df31.png"/>
 </div>
 </div>
 <p>There are there three different relevant amplitudes:</p>
@@ -738,22 +719,15 @@ <h2>Analysis of imblanced MZI’s<a class="headerlink" href="#analysis-of-imblan
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gdsfactory</span> <span class="k">as</span> <span class="nn">gf</span>
 
-<span class="n">PDK</span> <span class="o">=</span> <span class="n">gf</span><span class="o">.</span><span class="n">get_generic_pdk</span><span class="p">()</span>
-<span class="n">PDK</span><span class="o">.</span><span class="n">activate</span><span class="p">()</span>
-
 <span class="n">c</span> <span class="o">=</span> <span class="n">gf</span><span class="o">.</span><span class="n">Component</span><span class="p">(</span><span class="s2">"my_component"</span><span class="p">)</span>
 <span class="n">mzi</span> <span class="o">=</span> <span class="n">c</span> <span class="o">&lt;&lt;</span> <span class="n">gf</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">mzi</span><span class="p">(</span><span class="n">delta_length</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
 
-<span class="n">c</span><span class="o">.</span><span class="n">plot_matplotlib</span><span class="p">()</span>
+<span class="n">c</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><span class="-Color -Color-Green">2024-01-09 22:59:50.499</span> | <span class="-Color -Color-Bold">INFO    </span> | <span class="-Color -Color-Cyan">gdsfactory.technology.layer_views</span>:<span class="-Color -Color-Cyan">__init__</span>:<span class="-Color -Color-Cyan">785</span> - <span class="-Color -Color-Bold">Importing LayerViews from YAML file: '/home/sequoia/git/Photonics-Bootcamp/env/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.</span>
-<span class="-Color -Color-Green">2024-01-09 22:59:50.508</span> | <span class="-Color -Color-Bold">INFO    </span> | <span class="-Color -Color-Cyan">gdsfactory.pdk</span>:<span class="-Color -Color-Cyan">activate</span>:<span class="-Color -Color-Cyan">258</span> - <span class="-Color -Color-Bold">'generic' PDK is now active</span>
-</pre></div>
-</div>
-<img alt="../_images/9a415e85d0c43a7a40bed466a818eb08f4cae62e6a4b0a95e4be1e379298b378.png" src="../_images/9a415e85d0c43a7a40bed466a818eb08f4cae62e6a4b0a95e4be1e379298b378.png"/>
+<img alt="../_images/1bf7001c97363cb5f3fb2601b97ec9d6273a66cee4a8621048021573cda77d12.png" src="../_images/1bf7001c97363cb5f3fb2601b97ec9d6273a66cee4a8621048021573cda77d12.png"/>
 </div>
 </div>
 <section id="id1">
diff --git a/searchindex.js b/searchindex.js
index a46abb7..cc2d0fe 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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11, 20, 34, 39, 41, 42, 44], "b": [3, 6, 8, 42], "c": [3, 6, 8, 11, 21, 31], "d": [3, 7, 15, 31, 34, 40, 41, 42, 43], "e": [3, 6, 8, 11, 12, 18, 21, 27, 30, 33, 34, 36, 39, 40, 44], "return": [3, 6, 11, 19, 21, 34, 39, 42, 44], "3": [3, 8, 11, 14, 15, 18, 19, 20, 21, 22, 31, 33, 38, 39, 42, 44], "2": [3, 6, 8, 11, 14, 15, 18, 19, 20, 21, 31, 32, 33, 39, 42, 44], "now": [3, 6, 21, 23, 31, 32, 33, 34, 37, 39, 44], "just": [3, 6, 8, 11, 13, 14, 15, 17, 21, 27, 31, 34, 39, 44], "need": [3, 6, 11, 12, 21, 22, 31, 34, 36, 40, 41, 43, 44], "paramet": [3, 6, 8, 20, 21, 30, 33], "do": [3, 6, 15, 18, 19, 22, 34, 36, 37, 41], "curve_fit": 3, "scipi": [3, 12, 31], "take": [3, 6, 8, 11, 13, 19, 21, 28, 30, 33, 34, 41, 42, 43], "y": [3, 6, 8, 11, 20, 21, 25, 27, 34, 39, 41, 42], "an": [3, 6, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 28, 29, 30, 31, 32, 33, 39, 40, 41, 42, 44], "initi": [3, 6, 8, 21], "guess": 3, "It": [3, 6, 8, 11, 13, 14, 15, 16, 18, 19, 21, 22, 29, 31, 32, 33, 34, 37, 39, 40, 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17, 20, 30, 31, 32, 33, 34, 39, 41, 42, 43], "doe": [6, 8, 21, 22, 32, 41], "cross": [6, 8, 14, 15, 16, 18, 39, 40], "ani": [6, 8, 11, 13, 14, 18, 22, 29, 31, 32, 34, 36, 37, 39, 42, 44], "within": [6, 11, 13, 16, 20, 21, 27, 28, 29, 30, 32, 33, 34, 39, 40], "although": [6, 13, 18, 30, 36], "overlap": [6, 8, 33], "parallel": [6, 8, 11, 39, 41], "lie": 6, "upon": [6, 32, 42], "point": [6, 13, 32, 39], "exist": [6, 8, 13, 18, 22, 30, 31, 34, 39], "geometri": [6, 8, 11, 13, 14, 18, 19, 20, 32, 33, 39, 40, 44], "instead": [6, 11, 13, 14, 20, 21, 22, 29, 30, 31, 34, 40], "new": [6, 13, 15, 16, 18, 27, 30, 31, 43], "one": [6, 8, 11, 13, 14, 15, 18, 20, 21, 22, 27, 28, 29, 30, 31, 33, 36, 39, 40, 41, 44], "That": [6, 22, 41], "parameter": [6, 21, 30, 40], "call": [6, 8, 18, 20, 21, 28, 29, 30, 31, 32, 33, 34, 39, 41, 42], "even": [6, 8, 30, 31, 40], "hierarch": [6, 13], "nest": 6, "save": [6, 8, 11, 13, 14, 19, 20, 31, 33], "memori": [6, 13, 30], "becaus": [6, 8, 12, 14, 18, 20, 21, 22, 28, 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33, 34, 42, 44], "repeatedli": 6, "would": [6, 11, 13, 14, 18, 20, 21, 22, 30, 32, 34, 43], "good": [6, 21, 34], "requir": [6, 8, 18, 21, 22, 28, 29, 30, 31], "higher": [6, 18, 27, 28, 29, 32, 33, 34, 39, 40, 44], "level": [6, 14, 21, 31], "By": [6, 11, 14, 21, 29], "draw": 6, "bank": 6, "without": [6, 13, 21, 22, 28, 29, 30, 31, 32], "much": [6, 8, 11, 21, 32, 33, 34, 37], "pattern": [6, 14, 18, 21, 30, 32], "where": [6, 8, 11, 20, 21, 22, 28, 31, 33, 34, 41, 44], "across": [6, 13, 21], "compos": [6, 30], "interconnect": [6, 13], "compound": 6, "intermedi": 6, "specifi": [6, 8, 11, 13, 14, 19, 31, 34, 40], "direct": [6, 13, 19, 20, 25, 30, 32, 33, 34, 39, 41, 42, 44], "face": [6, 13], "either": [6, 11, 13, 30, 41, 44], "out": [6, 8, 11, 13, 14, 16, 20, 21, 31, 32, 33, 34, 39, 41, 44], "also": [6, 8, 11, 12, 13, 14, 15, 16, 20, 21, 22, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44], "width": [6, 8, 13, 14, 18, 19, 20, 21, 29, 30, 33, 39, 40], "attach": [6, 11, 13], "your": 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