diff --git a/docs/tutorials/discocirc-basics.ipynb b/docs/tutorials/discocirc-basics.ipynb
index ed850be..74d2cb2 100644
--- a/docs/tutorials/discocirc-basics.ipynb
+++ b/docs/tutorials/discocirc-basics.ipynb
@@ -88,7 +88,7 @@
     "When frames are present in a string diagram, `lambeq` uses a colour encoding for wires and frames to make the diagram more readable, as in the above image. By default, the colour of each frame indicates its \"type\", i.e. the number of nested boxes.\n",
     "```\n",
     "\n",
-    "Sometimes, an entity can interact with more than one actions. Check for example the following paragraph:"
+    "Sometimes, an entity can interact with more than one actions, as in the following example:"
    ]
   },
   {
@@ -122,7 +122,7 @@
     "\n",
     "## Parsing longer texts\n",
     "\n",
-    "The {py:class}`~lambeq.experimental.discocirc.DisCoCircReader` is efficient and robust enough to parse really long texts, for example entire book chapters, into a single diagram. However, keep in mind that such a diagram can quickly become very dense, to the point it's difficult to read. In fact, as you can see below, even relatively small and simple texts can generate fairly complicated diagrams."
+    "The {py:class}`~lambeq.experimental.discocirc.DisCoCircReader` is efficient and robust enough to parse really long texts, for example entire book chapters, into a single diagram. However, keep in mind that such a diagram can quickly become very dense, to the point it's difficult to read. In fact, as you can see below, even relatively short and simple texts can generate fairly complicated diagrams."
    ]
   },
   {
@@ -158,27 +158,27 @@
    "metadata": {},
    "source": [
     "```{note}\n",
-    "When trying to plot dense diagrams, increasing the size of the figure by using the `figsize` paramemeter in {py:meth}`~lambeq.backend.grammar.Diagram.draw` method can help.\n",
+    "When trying to plot dense diagrams, increasing the size of the figure by using the `figsize` paramemeter in {py:meth}`~lambeq.backend.grammar.Diagram.draw` method can increase significantly the readability of the result.\n",
     "```\n",
     "\n",
     "## Simplifying the diagram\n",
     "\n",
-    "**TBD\n",
+    "The {py:mod}`~lambeq.experimental.discocirc` package provides a few ways to abstact away some of the details of the text diagrams, which we introduce in the following sections.\n",
     "\n",
     "### Rewrite rules\n",
     "\n",
-    "**TBD"
+    "One way to avoid making your text diagrams excessively complex, is to reduce the amount of frame nesting in expressions that involve large chains of modifiers. Check the following example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 82,
    "id": "c35bea0c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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atdPHH39cZp9u/+OPP6p///5aunSpEe9zOJb7aPPmzerUqZNef/31Enlgbdp9JPFvCQBwbOz4eyGZ+zeDHQAAx6C4ewLJ2/KrCrLXKzq5oxzR1Up1XeXFgXMvX748/D9J27Zt0+TJk1WzZk1VqVIl/HIrRyhftQp+Uo6jurY5k497XaYp7uw/ZFfRxj1R6l57u2JcR37fzYmuCwBQfhzP73D38iUKRUYqJ7X4n1ln6t8MnpkBjiArK0upqany+/3Futw/W0Wpb9Mo3Td7t7K2F+8BqFT4uSAZGRnyer3FvmxZO977yFM1Qute7qL3vt2ioU8e/c3/f2fSfSQd//3k7DVOzhYXKm/SqdLuTUe/wN+Ydj8BQGV3vH8vJOnLDqn6IzdfFy4/vjAx8W8GMQMcRVZWlnw+X7Eus3HOC9qy6G01vfy/ctdqUux1ejweo36RHM995AzsVsrau7Uztq0217r06Bf4G9PuI+n47qcHvonVJ5kx+uAff6pWbPHD2MT7CQAqu+P5eyFJjtuul+ITFLz1/uNar4l/M3iZGXAUXq+32Bt2wY+J2iKpWbNm8jRsWypzlSfHcx+F9m6Xf23hh4jWNui1uSfieO4nz8psKXOPWrZsKW8Cv7IBoDI4nr8XkvSjy6WIGLeaVpK/qxJ7MwMAAABgKGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCSX3QMcr6ysLPl8PrvHAA5plzNBNdr0U+b6TcraHrR7nHLJCu5VYrUO2ru3hnakp9s9TrlVzxmlAY0jtHrlD/JFh+weBzgkj8cjr9dr9xgAKiEjYyYrK0upqany+/12jwIcxVt2D4AKYprdAwBH4Ha7lZGRQdAAKHNGxozP55Pf79drDz6h1IYpdo8DAECllbEmU4PvGCGfz0fMAChzRsbMfqkNU5SW2tLuMQAAAADYgB0AAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAKDMLlqfLmdZIZ99whd2jAACACoCYAVBmJn74tm4cNERfpX+nTVv+sHscAABgOGIGQJnY7d+jtz/7RNddcInOPq2npkx/L3zetp07dMnY4TqpZzvFdGymxgN6avKH79o4LQAAMIHRnzMDwBzvzPpEzZIbqWlyIw0+e6CGj7tfY4f9S5Zl6a5nx+vnNav06TOT5ameoMwN65STu9fukQEAQDlHzAAoExM/eEeDzx4oSerbubt27L5F85YsUo9TTlXW75t0crMWat+itSQpuW49GycFAACm4GVmAErdL+tW67ufluuiswZIklwuly4882xN/PBtSdJ15w/WW599pLYX9NMtTzysb5cttXNcAABgCJ6ZAVDqJn7wjgoKClTnjFPDp4VCIUVFRuqZ2+7VWV17aP2MrzXj6y/1+cL56nXNJbr+wkv12Mg7bJwaAACUdzwzA6BUFRQU6JWP39f4UXdo2dufhL+WvzNDdU6qqTdnfiRJOimhhoac8w+99tAETRjzb7047S2bJwcAAOUdz8wAKFUffzVH23bu1LCBFyiuarUi5/2jV19N/OAdbdr6h9qltlSLRk2Um5enj7/6QqkNGtk0MQAAMAXPzAAoVRM/fFu9O3Y5KGSkwphZ8vMKuZwujX16nFpfcJa6DbtQTodTbz36tA3TAgAAk/DMDIBS9dFTEw97XodWbRVatlaS9O9rbiqrkQAAQAXBMzMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMVCn3Zpa1eaN827fZPQZwXDzV4+WtXbfEr5ftAkBp/X4BgNJS6WIma/NGpZ53hvw5OXaPAhwXd0yMMt7/vEQfcLBdAJBK5/cLAJSmShczvu3b5M/J0WuvvabU1FS7x6lwxo4dq1mzZmn27NmKj4+3e5wKJyMjQ4MHD5Zv+7YSfbDBdlG27rrrLs2YMUMzZ87USSedZPc45dKqVas0aNAgXXzxxRo1apTd41QKpfX7BQBKU6WLmf1SU1OVlpZm9xgVzv6AadOmjTwej83ToLjYLspGQkKCJKl169aqXbu2zdOUTxEREZKkxMRE/k0CAA6LHQAAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBILrsHME1WVpZ8Pp/dY5Rb27ZtkyQtX75c8fHxNk9Tvnk8Hnm9XrvHKBFsF8WTnZ0tSVqxYoU2b95s8zTl06pVqyRJW7ZsUXp6us3TmKUi/W4BgKMhZoohKytLqamp8vv9do9S7vXu3dvuEco9t9utjIwM4x90sF0cv759+9o9Qrn3xhtv6I033rB7DKNUlN8tAHAsiJli8Pl88vv9Gj91oho1a2r3ODDY6pW/aNSQYfL5fMY/4Ni/XTz531eV0jTV7nGASi3zlwzdfM2lFeJ3CwAcC2LmODRq1lQt0062ewygXElpmqpWbdLsHgMAAFQi7AAAAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYn7OJeffXAyDHh491TUjX5yWdKdR0AAAAAMQMAAADASMQMAAAAACMRM5XQnE8+1cmeOgoEApKkn5ctV0pEFf3n9rvCy4y9+l8aedkV2vbnnxo+eIi61E9Ry2oe9Wt7ij56651ire/tiVN0sqeOvp0zV5L0648/6Yr+A9W6eqI61k3WqCHDlO3zhZf379mj0UOvVOvqieqU1FAvP/FkCdxqoOL4cvZMndf3NLWsH6/WDT0aeuEArVu7WpK0IWudvPEOffTBO/rHWd3UuLZb/U/voDWZv2p5+mKd3fMUNatXVZf9s5/+9G0NX2cwGNSE/9ynDi2SlFIzWn1PO1lfzp4ZPn//9X760fu6cMDpalKnivp0baul3y0oMtsbU19SxxZeNalTRVcNPk8vPfu4WtaPL5s7BgBQ6RAzldApXTtrz65d+vn75ZKk7776WvEejxbNmx9e5rv5X6tj927K3Zurlmkn66X/TdOMZYs16MorNHrolVr+3ZJjWteLjz2ux+74t6bMmK7Op/fUzu3bNfjMfmreto0+WDhfkz7+UL4tW3TTRZeGL/PIrXfou/lf64X339aUGdO1aN58/fT9shK9DwCT+f17dNX1I/Tx3MV683+z5XA4dPXg8xQMBsPLPPHIPbpp9B2a8eVSuVwu3XjVJXro7lt1z8MT9N6Mr7RubabGP/Tv8PKTXnhSLz3zuO68b5w++3q5up1+poZdfK7Wrl5VZN3jHrhTV98wSjO/+l4NUproxisvVkFBgSRp8cJvdPvI63TFtTdp5lff67QevfX0+IfK5k4BAFRKLrsHQNmrGhen1Datteirr9SqfZoWfTVfl998g56+/yHt2b1bu3bs1PrM1erYratq1a2jK0cOD1/2shuu0/zPZ2vGe9PUpkP7I67nP2Pv1Ievv6nXv5ipJi2aS5Jefe6/at62jUY/cG94uUdeekGnNWiitb+uUmKd2np38lSNnzpRnU/vKUkaN+lFdU1uUvJ3BGCofuf8o8jxx56ZqLYpiVq18me5Y2MlSVffMErde/WRJF1xzU264cqL9eb/ZuuUU7tIki4cfIXee3Nq+Dr++8x4XXfzLTrnH4MkSbff+6gWfP2lJj4/QQ889mx4uatvGKVefc6WJI287R717tRS69ZkKqVJM0158Rn16H2WrrlxtCSpYUoTLf1ugb747ONSuicAAJUdMVNJdejWVYvmzdewETdrydffavQD9+rTd6dpyTffakf2NtWsU1vJjVMUCAT0/CPjNOPdafpj02bl5+UpLzdX0W73Ea9/4oSnlLPHrw8Wzpe3YYPw6RkrftCiL79S6+qJB10ma80a7c3JUX5entp2OCV8evWEBDVo0rjkbjxguLWrV2n8Q3fr+6WLtC3bF35GZuNvWWrcrPA/DlJbtA4v70msKUlq1rxV+LSTEmvKt3WLJGnXzp36Y/Mmtd8XOvu179hZGT+uKHLagdebWKu2JOnPrVuU0qSZ1mT+oj79BxZZvk27U4gZAECpIWYqqY7du+m9Ka8qY/kKuSJcatSsqTp2P02L5s3Xzm3b1aFbV0nSS+Of0NSnn9Md4x9V05YtFFOlih4cdYvy8/KOeP2ndO2suTM+04z3punaW0aHT/fv3q3T+/fTmIfuP+gyibVraX3m6pK9oUAFdMVF56huUn09+uSLqlmrjoLBoM7o3Er5+X9tl66IiPBhy7IOeVrogJelHatDXW8wVPzrAQCgJPCemUpq//tmJj/5jDp0O01SYeAsmjdfi76ar47dukmSln67UL0GnK2Bl1yk1Dat5W3YQGtXZR71+lu3b69JH32g5x95TC8/PiF8eouT22rVzxmql1xfySmNiny5q1SRt1FDRUREaNl3i8OX2bFtm9YdwzqBymBb9p9aveoX3TjqDnXt3kuNm6Zqx/ZtJ3SdVatVU83adbRk4TdFTl+y6Fs1bpp6zNfTMKWplqcXfT/d348DAFCSiJlKKi4+Xk1btdT0N99Wx30xc8ppXfTz98u09tdV4WdmklMa6Zsv5ij924XKzFipO6+7Ub4/thzTOtI6n6qJ09/X0/c/HP4QzcHXXaPt2dkaPnioVixeqvWr1+irWZ/r1mHXKBAIqEpsrM6/fIgeve0OLZj7pX798SfdMuwaORz8UwUkKa56vOITauiNqS9p3ZpMffPVHN1/56gTvt5rbhyt55/8j6a//7ZWr/pFD99zm37+YZmuuPbmY76OoVffoLmfz9BLzz6utatX6bXJ/9WXsz+V9j2DAwBASeNlZpVYx25dlbF8hTp2L4yZ6gkJSkltJt+WLWrYtPAN99fffqs2rF2ny88+V9HuGA268gqdcU5/7dq585jW0b5rZ700fZquHHCenE6nLrvhOr0z7wv95/a7NLTfOcrLzVXd+l6ddmbvcLDc+uiD2rN7t64eeL6qVI3VsBE3adeOY1sfUNE5HA49M/FN3X3bzTqjcys1TGmqex99Uhf073lC13vFNTdp184deuCu0fpz6xY1btpcE9/4nxo0Ovb3q51yahc99PjzmvCf+zTuwbvU/fQ+uvK64Zry8rNHvzAAAMeBmKnE7nx8nO58fFyR0z5aurDI8eoJCXph2ttHvJ43vphZ5Pi8zIwixzuc1lUrtv/1bE5y4xQ99+6bh72+KrGxGj91YpHTrho14ogzAJXJaT16a87Cn4qclrUteMjDktSpa4+DTjv/4qE6/+Kh4eMOh0Mjbr1bI269+5DrTPImH3QdcXHVDzrt4iFX6eIhV4WP33LzVUpukHL0GwUAwHEgZgAAJea/Tz+m03qcIXeVKpo7+1NNe/OVIrt2BgCgJBEzAIASsyx9sV54apx2794lb3JD3fPIk7rosivtHgsAUEERMwCAEvP85CO/LBUAgJLELqIAAAAAGImYAQAAAGAkXmZWRjZlbVC2z2f3GChhCR6P6niT7B7DaBs3ZCk7m22jIklI8KhuktfuMQAAlQAxUwY2ZW1Qn1ZpyvH77R4FJSzG7dZnP6QTNMdp44YsnX5qc7aNCibG7dachT8TNACAUkfMlIFsn085fr9ee+01paam2j1OqRo7dqxmzZql2bNnKz4+3u5xSlVGRoYGDx6sbJ+PmDlO2dmVY9uYO3euRo8erTFjxmjQoEF2j1OqwttFto+YAQCUOmKmDKWmpiotLc3uMUrV/oBp06aNPB6PzdPAFBV928jKypIkJSUlVejbCQBAWWMHAAAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMJLL7gFwdFlZWfL5fHaPcUy2bdsmSVq+fLni4+NtnubYeDweeb1eu8dAMZm0XaxevVqStGHDBqWnp9s8zbFj2wAAlHfETDmXlZWl1NRU+f1+u0cplt69e9s9wjFzu93KyMjgQZtBTN0uxo0bp3Hjxtk9xjFj2wAAlHfETDnn8/nk9/s19dln1KxxY7vHqXBWrlqlIdffIJ/PxwM2g7BdlD62DQCACYgZQzRr3FhprVvbPQZQrrBdAABQubEDAAAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZ2GLeN98qolZtbd+xw+5RAFtccdPN+sfQoeHjvf7vPI286y77BgIAwEDEDI7qiptuVkSt2oqoVVsx9ZLU+JQOuu2++7V37167RwPKBUIEAAB7sGtmHJM+PXvq5ScnKD8/X+krVuiKm26WZVl6+K477R4NAAAAlRTPzOCYREVFqlZiopLq1tW5Z52lXt1O0+yv5kmSgsGgHn3qKTU+pYOqJjdQ2um9NO2jj4tc/tPZX6h55y6qmtxAvc/7h9Zt2GDHzQBK3BU33ayvFizQ0y+9HH4Gc/W6dbpqxMjwNtGiS1c99dJLdo8KAECFwzMzKLYfM1ZqweIl8tarJ0l69Kmn9Ma0aXr2P48qpWFDzV+wUENuuEEn1UhQt86dtWHjRp0/bJiuGzpUV146WEuXL9ct99xr860ASsYTD9yvVWvWqEWzprrnllskSfFxcapXu7befOlF1YhP0IIli3Xd6DGqnVhT5597js0TAwBQcRAzOCaffD5b1Rs2UkEgoNzcXDkcDj350IPKzc3VI08+pZnvvqNO7dtLkhrWr69vvvtOL736mrp17qz/Tp2qRvXra9y990iSmqak6MeMlRr3zDM23iKgZMRVq6bIiAi5Y2JUKzExfPrdt4wJH25Q36uFS5bqvenTiRkAAEoQMYNj0qNLFz3z6CPa4/fryf++KJfLpfP699dPK3+RPydHZ11wYZHl8/Lz1bZlS0nSylWrdEpaWpHzT23frsxmB+zw3KTJmvLWm9rw20bl7N2rvPx8tWnRwu6xAACoUIgZHJMq7hilNGggSXp5whNKO72XJr3xhlo0ayZJmv7aq6pTu3aRy0RFRpb5nEB58PaHH+rW++7Tf+6+W6e2b6eqsbEa/9xz+i79e7tHAwCgQiFmUGwOh0O33XyTxtx9j37+9htFRUUpa+NGdevc+ZDLN2vcWB9/NqvIaYuWLi2LUYEyERkZoUAgGD7+7XeL1al9e113+dDwaWvWrbdhMgAAKjb2Zobj8s8BA+R0OvXSK69q5HXXavTdd+uVt9/R6nXrlL5ihZ55eaJeefsdSdLVl12mVWvX6tZ779MvmZl68/33w+cBFUH9pCR9l56udVkb5PvzT6U0bKCly5dr1ty5+nX1at396KNasmyZ3WMCAFDhEDM4Li6XS/+64nI99uyzuvXGm3T7iBH6z9NPqdVp3dT/oov16Rez1cCbJEny1qund15+WdNnzlS7Xr314iuv6P6xY22+BUDJGXnddXI6nWrdvZtqt2ipM3v00MB+/XTxNdeqS7+z9Wf2Nl07dIjdYwIAUOHwMjMc1aSnnjzk6bfceKNuufFGSdJNV12lm6666rDXcfaZZ+jsM88octrQiwaV3JCAjZo0aqSvPyn62UoTn5ygiU9OKHLag3fcET789+3qiw/eL7X5AACoqHhmBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJHYm1kFl/Xbb/JlZ5fZ+nbu2qW8vDwlxMfL4SjbVvYkJMhbr16ZrhNmKuvtIjcvT7t27VIVdxXFxESX2XqlirVdbNyQpexsX5mtLxAIaMf2bYqMjFJs1apltl5JSkjwqG6St0zXCQAmImYqsKzfflOrbt3l9/vtHqVMuN1u/fDVvArzwA2lg+3CTBs3ZOn0U5srp5L83GLcbs1Z+DNBAwBHQcxUYL7sbPn9fr322mtKTU0tk3WOHTtWs2bN0uzZsxUfH18m65SkjIwMDR48WL7sbOMftKF02bFdzJ07V6NHj9aYMWM0aFDZfb5SRdousrN9yinjn9vmzZvVv39/nXPOObr77rvLZJ3SXz+37GwfMQMAR0HMVAKpqalKS0srk3XtD5g2bdrI4/GUyTqB41GW20VWVpYkKSkpqczWWVHZ8XOrUaMGPzcAKKfYAQAAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIzksnsAlF9ZWVny+XzFusy2bdskScuXL1d8fHyx1+nxeOT1eot9OaCsHM92sXr1aknShg0blJ6eXux1sl2cuOP5uW3evFmS9Oeff/JzA4ByipjBIWVlZSk1NVV+v/+4Lt+7d+/jupzb7VZGRgYPAGCboUOHavv27frwww8POu9Et4tx48Zp3Lhxxb4c28WJOdGf2/Tp0zV9+vRiX+5Ef25n92ivH9ZlKy6u+nFdHgAqA2IGGjp0qKZOnSpJcrlcqlevnrp16ya/368JEyaoQYMG2rlzp6pVqyan03nI61ixYoVuv/12vfXWW4qNjVVubq5ycnJUvXr1Y54jMzNTw4cPl8/n40EbyiWfzxfeLlJSUop12b9vI8eK7eLEncjP7XjxcwOAskHMQJLUt29fTZ48Wfn5+Vq6dKkGDx4sSUpJSVHLli2Pevldu3ZJklJTUxUXF1eqswJ2O9bt4kBsI/Y7np/biSooKCjT9QFAZcMOACBJioqKUq1atZSUlKSBAweqY8eO4fM2bNig5ORk/fTTT+HT5s6dq549e6pp06YaNGiQfvvttyLX9+6776pVq1bh40888YTOOussvf/+++rSpYtatWqlG264Qbt37w4vs/8lIF26dFHt2rX1xBNPqEePHho+fHgp3WpUZrNnz1arVq0UExOjGjVqqHfv3tqzZ0/4/Mcee0y1a9dWjRo1dP311ys/Pz983pw5czRgwAC1aNFC7du310033XTQ+zGOto1I0qeffqozzjhDTZo0UZcuXfTSSy+Fz5s6darOPPPMItdnWZZeeOGF8Gm9e/fWnXfeqXXr1snhcGjJkiVFrv+NN96QJAWDweO8l8qHL2fP1JgbhkmSTj/9dPXv3z/8PqR169bJsiy99dZb6ty5s6Kjo9WyZUvNmzcvfPn998vixYvVt29fNWnSRAMHDtQvv/xSZD2LFy/W+eefr6ZNm6pTp0665557irw07f333z/iz33BggVKTk7W3LlzdfPNN0uSli1bpmAwqIcfflgNGjRQTEyM2rRpo/fee6/IumfMmKEmTZooJiZGV199dQneewBQsREzOMiPP/6o5cuXH/b8TZs26ZprrlGvXr00Y8YMDRo0SI8++uhRrzcrK0uzZs3SpEmTNHHiRC1atEjPP/98+PyXX35ZUmH4fP7555o/f/5xvekWOBa33367rrjiCmVkZOjLL7/Ueeedp1AoJKkwHFavXq25c+dq6tSpmjJliqZMmRK+bEFBgUaOHKlPP/1UL774on777TeNHj06fP6xbCM//PCDrr/+eg0YMEAzZ87U8OHDNX78eL377ruSpI4dO2rVqlXasWOHJCk9PV0ej0dffvmlJCk/P18LFixQjx49lJycrN69e2vy5MlF1rH/fR4Oh9m/6v3+PRp4wSWSpOeff14Oh0P/93//VyTSxowZo1GjRun7779Xp06dNGDAAP35559FrmfSpEm64447NH36dNWoUUPDhg0LR+r69es1ZMgQ9e3bVzNnztQzzzyjxYsX69///nf48kf7ue/36KOPaujQoZKkxo0b6+GHH9Yrr7yiF154QT/99JNGjBihwYMHh4Nrw4YNOu+88zRgwAAtW7ZMAwcOLMm7DwAqNLP/wqHEfPzxx4qNjVV0dLRatWoV3ivZobz22muqX7++7rzzTjVq1EgDBw7UP//5z6OuIxgM6rHHHlPTpk3VoUMHnXfeefrmm28kSbt379acOXMkSR06dFDLli01efJkBQKBkrmBwN8EAgGdd955Sk5OVqtWrfSvf/0r/F6W+Ph4PfPMM2rWrJn69++vs88+W1988UX4smeeeaZ69uwpr9ertLQ03XPPPfryyy/Dz+wcyzby8ssvq0uXLrrpppvUsGFDnX/++RoyZIhefPFFSVLTpk1VvXp1/fDDD5KkpUuXatSoUeEHwN99953y8/PVuXNnSdKVV16pN998U7m5uZIK4yczM7MU78Gy0++cf6hLt9MlFd4vkyZN0g8//KCff/45vMwNN9ygf/zjH0pNTdXzzz+vuLg4TZw4scj1XHTRRTrttNPUrFkzjR8/Xj6fT5999pkk6bnnntO5556rYcOGqUGDBmrXrp3uuecevf/++9q7d68k6YILLjjiz32/kSNH6uSTT5YkxcTE6KGHHtKkSZPUp08fNWzYUEOHDtXgwYP13//+V1JhoDVq1Ejjx49X06ZN1a9fv9K5IwGgAiJmIEnq2bOnli1bpkWLFmnIkCEaMGDAYZfNzMxU27Zti5yWlpZ21HXUq1evyBufTzrppPD/nGZlZR302vK4uDg1bdq0GLcCOHYdOnRQq1atdP755+ull14qEvAtWrQosrOL2rVra8uWLeHjmZmZGjZsmDp37qwWLVrowgsvlFT4jMz+84+2jWRmZqpdu3ZFTmvXrp3WrVunQCAgy7LUoUOHcMysWbNG//rXv5Sbm6uVK1dq3rx5OuWUU+R2uyVJAwcOlNPp1AcffCBJmjJlitq3b38id1G5sXb1Kj163+2SpG7duik5OVlS4e+N/Tp16hQ+7HK51L59e2VkZBS5nmbNmoUPV69eXQ0bNgwHX0ZGhqZNm6bmzZuHvy677DIFg8HwSwR/+OGHI/7c92vdunX48IYNG+T3+3XGGWcoNjY2/PXKK6+EXyqXkZFR5KW9AIBjxw4AIEmqUqVKeC8/kyZNUpMmTUp8HS5X0X9ulmUZ/1p+mOu5557T3r17NWvWLD399NO64447tGjRIklSREREkWX//m/13//+t3r27KkJEyYoISFBmzZt0mWXXaa8vLwSnfHUU08Nv3SsadOmqlatmrp166Yvv/xS8+bNU/fu3cPLRkZG6rLLLtPkyZN13nnn6Y033tDw4cO1ePHiEp3JDldcdI7iqhd+btXUqVPVrFkztWzZskTv7z179ujiiy8OvzzsQHXq1JHf79dll12mbt26HfXnHhMTEz68/z03n3zyierWrVtkuaioqBKbHwAqK56ZwUEcDoeuuOIKSQq/ZOVAKSkpB72n5vvvvz+hdXq93oNiZ8eOHfr1119P6HqBw7EsS126dNG9996r77//XpGRkeFnNY5m586duvXWW9WhQwelpKQc9N6MY9lGUlJStHTp0iKnLV26VA0aNAg/K9SxY0dt2LBBksLP4vTo0UOzZ8/WN998ox49ehS5/JVXXqnZs2frueeeU0FBgU4//fRjuj3l2bbsP7V61S+68NLCHQA0aNDgkC+DXbhwYfhwQUGBli5dqtTU1CLLHPiG/x07dmjt2rXh/8Rp2bKlVq1apeTk5IO+IiMjtXr1am3btu2IP/dDadiwoaKiopSVlaWUlJQiX0lJSZIK93D33XffFf/OAQAQMzi0/R96+cknnxx03iWXXKJ169bpoYce0urVq/W///3voD3zFFdsbGz4gdfixYv1008/adiwYXI4HLIs64SuGziUSZMmacmSJcrKytL777+vrVu3HvTg93BcLpemTp2qrKwsff7553r66aeLnH8s28hVV12lb775Rk899ZTWrFmj9957T1OnTtVVV10VXiY1NTX80sz9Lxnr0aOHPvzwQ+Xm5qpLly5FrjM1NVWnnnqqbr31Vl100UWKjo4u9v1S3sRVj1d8Qg3N/Oh9SYXvFRo5cuRByz377LP64IMPtHLlSl1//fXatm1b+D9l9nvzzTf1zTff6JdfftGoUaOUkJAQ3mPctddeq6VLl+rf//63fvrpJ61du1azZs0K7wCgTp06ioyMPOLP/VCqVKmi0aNHa8SIEZo6dapWr16t9PR0Pf300+HP97r22mu1atUqjRkzRr/88os+/fTTE7rPAKAyIWZwSPufJZk2bZpycnKKnFe3bl09//zzmjVrls466yy9/vrrGjNmzAmv88orr5QkDR8+XL1791aXLl2UmppaIR6QofxJT09Xv3791KRJE915550aP368zjrrrGO67IgRI/TJJ5+od+/eev7553X77bcXOf9YtpGWLVvq2Wef1UcffaQ+ffroiSee0MiRI3X++eeHl7EsSy1atJCk8HtwWrdurWrVqql9+/aqUqXKQbMNGzZMeXl5Bz2QN5XD4dAzE99U5q8rJUmPP/64xo0bd9ByjzzyiB555BG1adNGX3/9taZPny6Px1NkmaFDh+ree+/VgAEDtHXrVr388suKjIyUVBiCb7/9ttauXasLLrhAZ599th5//HHVrFlTklSjRg2NGzfuiD/3w7n//vt111136eGHH1Zqaqr69u2rTz75RA0aNJBU+Mz0tGnT9OGHH6pNmzaaNm3acd9fAFDZ8J4ZFNnl7N+9/vrratKkidatW1fk9F69eqlXr15FTrvgggvCh88///wiD8pGjBihESNGFFl+2LBhGjZsWPj4/jcyf/PNN0pLS9OePXt077338pkLKBXPPPPMIXdccajtYcKECZIU3lV49+7ddf311xdZprjbiCSdddZZRw2oO++8U/379w9vHw6HQ9nZ2YddfuPGjWrVqpVOOeWUCrNr89N69NYLU9/V2T3a66233lJaWlp4N9r77/fU1NTwe54Op3nz5po1a9Zhz2/Tpo1effXVw55/7rnn6txzzy1y2oE/906dOoWP7395oFQYpTfffHP4s2cOpX///urfv7+kwn9n7dq10ydfLlFcXPUj3CIAADGDIyrurl0XLVqkDRs2qH///sV+RmX/52fsfxBw3333SdJBDx4AuxV3u9i4caMWLFigtm3bht+jUdLr2r17t9atW6dnnnlGDzzwQLHWUVmsWbOmyB4Vj8bv92vGjBlKTk4u9p7hKspusQGgvCNmcEgej0dut1vDhw8/rssf6dmeI7EsSxdffLGioqLUrl07zZ8//6CXigB2OdHt4ni53e6jbgc33HCD3nzzTQ0cOLDCvMSspMTFxUnSMb8srKQcy88NAHBiiBkcktfrVUZGhnw+X7EuN3bsWM2aNUuzZ89WfHx8sdfr8Xjk9XqLfTmgLBzvdjF37lyNHj1aY8aM0aBBg4q93mPZLqZMmXLc/4lgsuTk5PBLzg7nwgsvVKdOnYr9c9u8ebP69++vc845R3fffXexZ+P3GQCUPmIGh+X1eov9h3h/wLRp04b/kUSFdDzbxf4Pd0xKSjqmD5hFyTuRn1uNGjX4uQFAOcXezAAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCSX3QOg9GVkZJTZunJzcxUXF6cVK1aoevXqZbbesryNqBjK8t9MVlaW4uLi9Mcffyg9Pb3M1lsRt4uyvE1//PGH4uLi5Pf7+bkBQDlFzFRgnoQEud1uDR48uMzX3atXrzJfp9vtlichoczXC7PYuV08+OCDevDBB8t0nRVlu0hI8CjGpp/b22+/rbfffrtM1xnjdishwVOm6wQAExEzFZi3Xj398NU8+bKz7R6lTHgSEuStV8/uMVDOsV2YqW6SV3MW/qzsbJ/do5SJhASP6iZ57R4DAMo9YqaC89arVyEeyAAlie3CTHWTvDzABwAUwQ4AAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEZy2T0Ajs3KVavsHqFC4n41Gz+/0sN9CwAwATFTznk8Hrndbg25/ga7R6mw3G63PB6P3WOgGNguygbbBgCgvCNmyjmv16uMjAz5fD67RzkmY8eO1axZszR79mzFx8fbPc4x8Xg88nq9do+BYjBtu5g7d65Gjx6tMWPGaNCgQXaPc8zYNgAA5R0xYwCv12vMA4r9AdOmTRv+RxelyqTtIisrS5KUlJSktLQ0m6cBAKDiYAcAAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEguuweoTDIyMuweodRFR0erSZMm+vHHH1WtWjW7xylVleHnWVYq+n35xx9/qEmTJtq1a5fS09PtHqdUVfSfJQCgfCFmykCCx6MYt1uDBw+2e5Qy07NnT7tHKBMxbrcSPB67xzBWQkLl2jbuuOMO3XHHHXaPUepi3G4lJLBdAABKHzFTBup4k/TZD+nK9vnsHgUlLMHjUR1vkt1jGKtukldzFv6s7Gy2jYokIcGjukleu8cAAFQCxEwZqeNN4kEvcAh1k7w88AUAAMeFHQAAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjOSyewATrV75i90jwHAV8d9Q5i8Zdo8AVHpshwAqG2KmGDwej9xut0YNGWb3KKgA3G63PB6P3WOcsP3bxc3XXGr3KABUcX63AMCxIGaKwev1KiMjQz6fz+5Ryq2xY8dq1qxZmj17tuLj4+0ep1zzeDzyer12j3HC2C6K76677tKMGTM0c+ZMnXTSSXaPUy6tWrVKgwYN0sUXX6xRo0bZPY5RKsrvFgA4FsRMMXm9Xv5IHMH+gGnTpg3/M1iJsF0UT0JCgiSpdevWql27ts3TlE8RERGSpMTERKWlpdk8DQCgvGIHAAAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBILrsHsEtGRobdI1RIiYmJ6tKli3766SdVrVrV7nEqnNL+d8t2UTZq1KihLl26aOXKldq8ebPd45RLmzZtUpcuXVSlShWlp6fbPU6lwPYPwESVLmY81ePljonR4MGD7R6lQuvRo4fdI1RY7pgYearHl+h1sl3Y4/TTT7d7hHLvm2++0YMPPmj3GJVGafx+AYDSVOlixlu7rjLe/1y+7dvsHgU4Lp7q8fLWrlui18l2AUAqnd8vAFCaKl3MSIUP3PhlDRTFdgEAAEzDDgAAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARnLZPcCJyFiTafcIAABUavwtBmAnI2PG4/HI7XZr8B0j7B4FOKReKZHq6I3QpMU5+n1X0O5xyqXqVVz6YGxrzf1hm+57e63d45RbjlNukqNBLxV8fKXk32r3OMAhud1ueTweu8cAUAkZGTNer1cZGRny+Xx2jwIc0sY5L2jLorf17luvy12rid3jlEvOwG6lrL1bTU/upnNvudTuccqtB76J1SeZMfrk01mqFUsYo3zyeDzyer12jwGgEjIyZqTCoOEXJ8qrgh8TtUVSs2bN5GnY1u5xyqXQ3u3yr5Xi4+NVOy3N7nHKLc/KbClzj1q2bClvgrG/sgEAKBXsAAAAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYA2CIUESNHQnOF9mYrFAraPU65FAqF9Nv2ArX3RqpaNL+uAQD4O/46ArCFwxklK8Kt4Nblyvv+aYXydtk9UrmyIyeoWz7cri9+yVV0hKXqbn5dAwDwdy67BwBQeUWdepdy5tyggswPVbB2plz1eyui8XlyxDWwezTbZPyer+fn79Zr3+3RnryQGtRw6q0ratg9FgAA5RIxA8A2VlQ1xfR6TgVrP1V+5ocqWPOxCtZ8LKtashxxDeSolixHXH05qjWQFVtXlsNp98glpiAQ0mpfgVb+nq+ff8/Xz78X6OfN+fpxc74kqX6CU3eeFquhHasooUrFud0AAJQkYgaArazIWEU0PV+uJv9QYPMiFWT+T4E/f1Rg5zoFDlzQESGrapIc1eoXRk5sbSkiVlZEFSnCLctVRVaEW4qoIsth36+2/EBIu/YGtXNvSDsP/J4T1No/C5Txe4F+/j1fv27JV25B0ctWjbLUu1m0rukaq7NbRMvpsOy5EQAAGIKYAVAuWJZDrjqd5KrTSaFQSKEcn4I71ym0Y52CO9cruHOdgjvXKbBjTdHIOYSQI0ohl1sBp1v5llt5llt75VZOKEZ5oQgF5VAw5FBQloIhhwJyKCiHAiGHfgvW1y874hSQQ4GgpYAcCoUKvwdClgIhh/KCTu3Mj9TO/AjtynPu+7K0Y6+Uk3/021o1ylLbepFKrRmh1NoRal7LpdRaEapX3SnLImAAADhWxAyAcseyLFnuk+RwnyTVOkWStHNvUBmb87T2tz+0fcsaBf1bpAK/nIE9cgX9igjmKFp7FGPlqKozR9VcOarq9Kuq809Vd+095nV//NuVenZ9m2NePtaRo6qunarmzFHdqBxVrZKjaq48VYssUNXIgKpFBlQ1SqoWJdWLd6l5vTgl1a0rZ1yyrMjYYt83AADgL8QMgHJlT25QP27O18+b85Wx770kGb/n67ft+5+PcUhK2fclxURYqhZtqWq0Q9WiLVWLdqia06Fqrr9Oi4sOKSEyT/GRfrmdBXJYktMKyqGAXFZQTitUeNwK6qKmLp0f3CGHFSp8vsYKyKnC850KyGEFFaU8VXXsVhVrl5wBv0L5e6SCwu+h/D1Svl+hAr+Uv1uhfL8UyC0c3S/pVyn318KjVoxHjmrJ+94jlLzvPUINZUXElPG9DgCAmYgZAOXCL3/8tRevXbmhIufVT3CqT2q0mteOUGpNl5rXjlCDGi5Vi3Yo0lX+X5YVChZI+XsU3PO7gjvXFnnpXOCPJdIfS/5a2BUtV/0zFZHyf3LEJds2MwAAJiBmANhqwdpcPTBzp2avLHwpmDfeqSs6xahlnUil1nKpWc0IVTX8AyMth0uKipMzKk7OhKZFzgvl5yi4KyscOQW/faWC1dNVsHq6HIlpimx+qZyJbe0ZHACAco6YAWCb7zfkqd9zW+XPC6lH4yj967RYnd0yRi5n+X+2paRYETFyJjQNR05Eq6sU2LxQ+ZkfKPjHUu3980dF93hCzhrNbZ4UAIDyh5gBYItQKKRBk33KyQ/p3Ss9OqcV7xORJMvhlKtuF7nqdlHB5u+U+/VY5X57t2L6v8OezgAA+BtiBoAtdu0NKsXjUteGUYTMYbhqd1Cg4dkK7vpNob1/yorx2D0SAADlitkvRAdgrNyANPuXXOUWhI6+cGXmiFJwy/cK7d1u9yQAAJQ7xAwAlGOO2DqSpOD2VTZPAgBA+UPMAEA55qzZTpIU+H3JUZYEAKDyIWYAoByzqibJcicq8McShQpy7R4HAIByhZgBgHLMsiy56p8p5e1U/q/v2D0OAADlCjEDAOVcRLOLZUXXUH7GGwr6t9o9DgAA5QYxAwDlnBURo4jWV0uBvcpdcK9CBTl2jwQAQLlAzACAAVz1e8uV3EfBP39S7rd3KxTIt3skAABsR8wAgAEsy6HI9mPkrNNFgd8XFwZN3m67xwIAwFbEDAAYwnI4FdXp33LWOkWBzQuU8/nVCmSvtHssAABsQ8wAgEEsZ6Siuj6siBZDFfL/ob1zblTeyjd52RkAoFIiZgDAMJbDqcgWQxTdfbysyDjlr3hROTOHqiBrjkKhoN3jAQBQZogZADCUM7GtYvpMkqvJBQrlbFXuwvu1d/Z1Kti8UKFgwO7xAAAodS67BwAAHD8rqpqi2l6niMb/p/wfJ6lg/Wzlzh8rK+YkuZL7ytWgrxyxdeweEwCAUkHMAEAF4KhSS1Edb1dE00HKX/ORCtbPVn7Gq8rPeFWOxJMV0eAsOet2k+WKsntUAABKDDEDABWIo3pDRaXdrMjW1yqwcb7y136q4JZ05W75Xop4Uq6kHnImpslxUls5YhLsHhcAgBNCzABABWS5ogo/aLN+bwV3b1bBuk9VsHamCtZ8ooI1nxQuUzVJzpPayHlSGzlOaiOH+ySbpwYAoHiIGQCo4ByxtRXZ8gpFNB+i4PZMBbcuV2DLMgV8K1Sw5mMVrPlYkmTF1i2Mm8S2++Im0ebJAQA4MmIGACoJy+GUM6GpnAlNFdH0AoWCAQV3rFFwyzIFti4vjJu1M1Swdkbh8lVqFz5rE9dQVtV6clRNklWlliwHfzoAAOUDf5EAoJKyHE454xvLGd9YEU3PVygUVGjH2sJnbfbHzbqZf7uQU1ZsHTli6+0LnH2RE1tPVoxHlmXZc2MAAJUSMQMAkCRZlkNW9UZyVG+kiCb/KIybXb8puCtLwV0b9h3+TaHdGxTYvEDa/LcrcEbLUbWurNh9gVO1nhxVasuKqi4rKk6KrCrL4uPNAAAlh5gBABySZTlkVfPKUc170HmhvN0K7t6o0K4NCu76TcHdf8WOtq/WIT+y03IURk1U9XDgWIc8XPilyKqyHM7SvpkAAIMRMwCAYrMiY+VMaColNC1yeigUUmjvNoV2F0ZOyP+HQrk7FMrdvu9rh0J7sxXasU5S6GhrkaKq/RU7LrfkjJLljJScUfsOR0n7ju8/bO0778Bl/zotsuhhXhYHAEYjZgAAJcayLFkxCVJMgpwntTnscqFgQMrb+VfgHBg7fzus3O0K7toghYIlP7AjQrKckmVJlkPSvu+WJclRGDuHOL3o8oWHrfAyjqKXOSCgih1iruhDLBclOSIIMQAQMQMAsIHlcErR8bKi449p+VAoJAXypECuQoHcvx3OVWjf8QPP339e4eG9h1gur/B6QkFJocLvoZCkYOH6DnF64eFA4fEiyx/mMqGAFMwv+TvQ4ZIVGXf4l+1Fx8mKiuf9SgAqPGIGAFDuWZYluaIkV5RMez4iFAoeEF9Hiq6/zj9qsOX7FcrdrmCOT9qx5uhDWA5pX/wUCZ/oeFlVasoRmyRH1XqF4QMABiFmAAAoRZblkFzRhS8ZK4XrDwULFMrdKYVfnneol+tt++v7zvU67PuVIqsV7m47tp6sqvsCp2o9OWLrynLFlML0AHBiiBkAAAxmOVzh9ykdi8L3K+3aFzrbFNy9qXBPdLt/U3DXBgW3rVLwz58PXk/MSeEPTw1/zlBcA1numrx/B4BtiBkAACqRwvcrVZcVXV1SspyJJxc5PxQMKOTfouDu3/btbnuDQrsLd7sd3LJMwS3fF72+mJPkOKmNnCe1kTOxrazYusQNgDJDzAAAgDDL4ZQVW1uO2NpSrVOKnBcK5Cm0e1PRZ3G2Llcga7YCWbMLLx9do2jcVE0ibgCUGmIGAAAcE8sZKSsuWY645PBpoVBIod2/KbBluQJblxXGzYY5CmyYU3iZ6Hg5PK3/iptqycQNgBJDzAAAgONmWda+nQUkKaJR/8K42bNJgS37wmbrcgV+m6fAb/MKl69WXxEN+slV/4xj3jU3ABwOMQMAAEqMZVmyYuvKEVtXanj2vrj5vTBqfl+swKavlbf8eeWteFHOOp3lanCWnLU6FL6XBwCKiZgBAAClpjBuCt+DE9Ggr0J5u1SQ9YUK1s5QYON8BTbOlxXjkav+mXI1GiBHlVp2jwzAIMQMAAAoM1ZkVUWkDFREykAFtmWqYO0MFWTNVv7KN5T/67typQxUZOolfIAngGPisHsAAABQOTnjUxSVdpPcA95TVMfbZblPUsGv78o/4xLlZbypUEGu3SMCKOeIGQAAYCvLGSlX/TMU02eKIk++UXK4lP/Di8qZfa2CO9bZPR6AcoyYAQAA5YLljFBE4/Pk7ve6XI3/odDOdcqZfa3y18xQKBSyezwA5RAxAwAAyhUrooqiTr5BUV0ekJyRylsyTnnpEwgaAAchZgAAQLnkqttFMWe+LEd8ExWsnq685S8QNACKIGYAAEC55XAnKrrbf2RVS1bBr+8oP+N1u0cCUI4QMwAAoFyzouIU3X2crCq1lf/jRAX+/NnukQCUE8QMAAAo9xwxHkV1vF2SlPf9MwqFgjZPBKA8IGYAAIARnJ6Wcnp7KZidoUDWF3aPA6AcIGYAAIAxIltdJUnKXzfL5kkAlAfEDAAAMIajSk05qqco6FuhUCDP7nEA2IyYAQAARnHWbC8F8hT0/WD3KABsRswAAACjOOIbS5KCuzfaPAkAu7nsHgAAAFReWVlZ8vl8xbpMZN5eVYvvrd1/BLV3R3opTQaYx+rWW/nRMUpPP77twuPxyOv1lvBUpYuYAQAAtsjKylJqaqr8fr/dowCQ5Ha7lZGRYVTQEDMAAMAWPp9Pfr9f46dOVKNmTe0eB6jUVq/8RaOGDJPP5yNmAAAAjlWjZk3VMu1ku8cAYCB2AAAAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAACJvzyac62VNHgUBAkvTzsuVKiaii/9x+V3iZsVf/SyMvu0Lb/vxTwwcPUZf6KWpZzaN+bU/RR2+9U+T6Pp32gfq1PUUtqtZQ+5pJuqzP2fLv2VOmtwkVFzEDAACAsFO6dtaeXbv08/fLJUnfffW14j0eLZo3P7zMd/O/Vsfu3ZS7N1ct007WS/+bphnLFmvQlVdo9NArtfy7JZKkLZs3a8Tgofrn0Mv02Q/pen32pzpz4LkKhUK23DZUPHxoJgAAAMKqxsUptU1rLfrqK7Vqn6ZFX83X5TffoKfvf0h7du/Wrh07tT5ztTp266padevoypHDw5e97IbrNP/z2Zrx3jS16dBeWzb/roKCAvX5v3NVt37hp8o3bdXSpluGiohnZgAAAFBEh25dtWjefIVCIS35+lv1GXiOUpo11ZJvvtV3X81XzTq1ldw4RYFAQM88+Ij6tT1F7RLrqXX1RM2fNVubNvwmSUpt01qdT++hfid30A2DBuutlydrx7ZtNt86VCTEDAAAAIro2L2blnyzQBnLV8gV4VKjZk3VsftpWjRvvr776mt16NZVkvTS+Cc09enndPWYkXrt8xmavmSBTjuzt/Lz8iRJTqdTU2d+rEkffaCU1GZ69dnndUaLttqwdp2Ntw4VCTEDAACAIva/b2byk8+oQ7fTJBUGzqJ587Xoq/nq2K2bJGnptwvVa8DZGnjJRUpt01rehg20dlVmkeuyLEvtunTS8Lvv1PQlCxQRGalZH04v89uEiomYAQAAQBFx8fFq2qqlpr/5tjrui5lTTuuin79fprW/rgo/M5Oc0kjffDFH6d8uVGbGSt153Y3y/bElfD3LFi3Wc4+M0w9L0rUpa4M+++B/yt7qU0pqU1tuFyoedgAAAACAg3Ts1lUZy1eoY/fCmKmekKCU1Gbybdmihk2bSJKuv/1WbVi7Tpeffa6i3TEadOUVOuOc/tq1c6ckKbZaVS2e/7WmPPWMdu/cpbr1vRr7n4fVvW8f224XKhZiBgAAAAe58/FxuvPxcUVO+2jpwiLHqyck6IVpbx/2OlJSm2nyJ/8rlfkAiZeZAQAAADAUMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBJ7MwMAACgFm7I2KNvns3sMlLAEj0d1vEl2j4F9iBkAAIAStilrg/q0SlOO32/3KChhMW63PvshnaApJ4gZAACAEpbt8ynH79drr72m1NRUu8cpNVu3blXfvn3Vr18/3X///XaPU+oyMjI0ePBgZft8xEw5QcwAAACUktTUVKWlpdk9RqnZvHmzJCkhIaFC306UX+wAAAAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARnLZPQAAAACKysrKks/ns3uMo9q6daskKTs7W+np6TZPc+w8Ho+8Xq/dY6AEEDMAAADlSFZWllJTU+X3++0e5ZjNmDFDM2bMsHuMY+Z2u5WRkUHQVADEDAAAQDni8/nk9/s19dln1KxxY7vHqXBWrlqlIdffIJ/PR8xUAMQMAABAOdSscWOltW5t9xhAucYOAAAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAcFzWZW1QRK3aWvbjj3aPgkqKmAEAAMAhXXHTzYqoVTv8VTO1uc6+6CKt+PlnSVJS3TrasGK5WjZrZvOkqKyIGQAAABxWn549tWHFcm1YsVyfvfuOXE6XBl56qSTJ6XSqVmKiXC4+7QP2IGYAAABwWFFRkaqVmKhaiYlq27Klxtx4gzZs3KStPt8hX2b20WefKbVTZ8XWT1bv8/6hV95+RxG1amv7jh023gpUVMQMAAAAjsnuPXv0xrRpSmnQQDUSEg46f+36LF145VU6p29fLf1itq667FL9+5FHbJgUlQXPCQIAAOCwPvl8tqo3bCRJ2uP3q3bNmvrw1VfkcBz8f+IvvfqqmjRqpEfv/rckqWlKin5auVIPT3iyTGdG5cEzMwAAADisHl26aMkXs7Xki9n69tNPdUaPHhpw8SVav2HDQcv+ujpT7du2LXLaKSefXEaTojLimRkAAAAcVhV3jFIaNAgfT2s9XjUaN9HE11/XFRdfYuNkAM/MAAAAoBgsy5LD4VBOzt6DzmvSKEVLly8vctqSZcvKaDJURsQMAAAADis3N0+/b9mi37dsUcavv+rm2+/Q7j171P/MMw9a9qpLL9UvmZkae/8D+nX1ar37v+l65e13JBVGEFDSiBkAAAAc1mdz5yqpdRsltW6jLv3O1pJly/TWSy+qe5fOBy3boL5Xb7/8kj6cMUNpp/fSf6dO1W033yRJioqMLOvRUQnwnhkAAAAc0qSnntSkpw6/J7Jkb5Lyf99c5LQBffpoQJ8+4eMPT5igenXqKDo6utTmROVFzAAAAKDEPD95itq3basaCfH69rvvNP655/WvKy63eyxUUMQMAAAASkzm2jV6eMIEZW/fLm/duhpx7TW69aab7B4LFRQxAwAAgBIz/r77NP6+++weA5UEOwAAAAAAYCRiBgAAAICReJkZAABABZH122/yZWeX2foCgYC279ypqMhIxVapUmbrlSRPQoK89eqV6TpR/hAzAAAAFUDWb7+pVbfu8vv9do9SJtxut374ah5BU8kRMwAAABWALztbfr9fr732mlJTU8tknVu3blXfvn3Vr18/3X///WWyTknKyMjQ4MGD5cvOJmYqOWIGAACgAklNTVVaWlqZrGvz5sIPzExISCizdQIHYgcAAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwksvuAQAAAGC/rKws+Xy+Yl1m69atkqTs7Gylp6cXe50ej0der7fYlwP2I2YAAAAquaysLKWmpsrv9x/X5WfMmKEZM2YU+3Jut1sZGRkEDY4bMQMAAFDJ+Xw++f1+TZgwQSkpKXriiSf0xRdfhM+vWrWqGjdurMsvv1wNGjQ47vW8/vrrWrhwoZ5++mllZmZq+PDh8vl8xAyOGzEDAAAASVJKSopatmyp6tWrq3v37ho3bpykwpeTjR8/Xg8//LC+/fbb477+xMRERUdHq2XLliU1Mio5dgAAAACAg0RGRioxMVGJiYlq0aKFrrvuOm3atEl//vmnJOnhhx9Wz5491axZM5122mkaP3688vPzi1zHc889p/bt26tFixa65ZZblJuba8dNQQXGMzMAAAA4oj179uiDDz5QcnKy4uPjJUmxsbF67LHHlJiYqF9++UW33XabqlSpomuvvVaS9PHHH2vChAm6//771b59e33wwQeaMmWKkpKS7LwpqGCIGQAAABxkzpw5at68uSTJ7/crMTFRkyZNksNR+MKeG2+8MbxsUlKSrr76an300UfhmJk0aZIuvPBCXXjhhZKk0aNH6+uvv+bZGZQoYgYAAAAH6dSpkx544AFJ0o4dO/Tqq69q6NCh+vDDD1WvXj199NFHmjJlitavXy+/36+CggJVrVo1fPnMzExdcsklRa4zLS1NCxYsKNPbgYqNmAEAAMBBYmJilJycHD7esmVLtWrVSm+99ZZ69uyp4cOHa8SIEerWrZuqVq2qjz76SC+99JJ9A6NSYgcAAAAAOCrLsuRwOLR3716lp6erbt26uuGGG9S6dWs1aNBAGzduLLJ8SkqKli1bVuS077//vgwnRmXAMzMAAAA4SF5enrZs2SJJ2rlzp6ZOnao9e/aoV69e2r17tzZt2qTp06erTZs2mjNnjj777LMil7/88ss1evRotWrVSu3bt9eHH36oVatWsQMAlChiBgAAAAeZN2+eOnToIKlwz2WNGjXSc889p06dOkmShg0bprvvvlt5eXnq2bOnbrzxRk2YMCF8+QEDBigrK0uPPPKIcnNz1bdvX11yySX66quv7Lg5qKCIGQAAABQxfvx4jR8//ojLjB07VmPHji1y2rBhw4ocv/7663X99dcfdDmgpBAzAAAAkFS4B7LiyMnJ0aeffqqkpCSdcsoppbou4FCIGQAAgErO4/HI7XZr+PDhZbpet9stj8dTputExULMAAAAVHJer1cZGRny+XzFutzWrVvVt29f9evXT/fff3+x1+vxeOT1eot9OWA/YgYAAADyer3FDovNmzdLkhISEpSWllYaYwFHxOfMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASC67BwAAAEDJycjIKLN1+Xw+VatWTTk5OUpPTy+z9ZblbUT5RswAAABUAJ6EBLndbg0ePLjM1z1t2jRNmzatTNfpdrvlSUgo03Wi/CFmAAAAKgBvvXr64at58mVn2z1KmfAkJMhbr57dY8BmxAwAAEAF4a1Xjwf4qFTYAQAAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASC67BwAAAMDBVq5aZfcIFRL3a8VCzAAAAJQjHo9HbrdbQ66/we5RKiy32y2Px2P3GCgBxAwAAEA54vV6lZGRIZ/PZ/coR7V161b17dtX/fr10/3332/3OMfM4/HI6/XaPQZKADEDAABQzni9XiMebG/evFmSlJCQoLS0NJunQWXEDgAAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkVx2DwAAAFBRZWRk2D1Cqdq2bZsaNWokl8ul9PR0u8cpdRX952kiYgYAAKCEJXg8inG7NXjwYLtHKROrV6/WlClT7B6jTMS43UrweOweA/sQMwAAACWsjjdJn/2Qrmyfz+5RUMISPB7V8SbZPQb2IWYAAABKQR1vEg96gVLGDgAAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARiJmAAAAABiJmAEAAABgJGIGAAAAgJGIGQAAAABGImYAAAAAGImYAQAAAGAkYgYAAACAkYgZAAAAAEYiZgAAAAAYiZgBAAAAYCRiBgAAAICRiBkAAAAARnLZPQAAAKjcVq/8xe4RgErP1O2QmAEAALbweDxyu90aNWSY3aMY4+FLGyk2xqkbX/zV7lHKrXbVqmhQ7Rp6e/OfWrJzj93jGMXtdsvj8dg9RrEQMwAAwBZer1cZGRny+Xx2j2KM5Kxxcgb2qPM1b9o9SrllffeNHK+9pF43j1KoY1e7xzGKx+OR1+u1e4xiIWYAAIBtvF6vcQ+e7OTfEi3l5SotLc3uUcqt7D9+0wZJ9ZPrK4H7qcJjBwAAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzACwVSgUsnsEADAGvzOBoogZALaIjbR0bqtofbMmT0/O3aWM3/MVDPJHGgD+LhQsUCD7F+X9MFHK3yVn3dPsHgkoN1x2DwCgcoqJdKh7k2jN+TVXt3y4Xbd8KFWPsXRK/Sidmhypxoku1a3uUt3qTtWJcyrKZdk9MgCUqlDBXoVytirk36pgzlaFdq5XwPeTgtt+kQK5kiQrxiNnrQ42TwqUH8QMANtc362qLkhza/qKHC1al6eF63L1+cq9+nzl3oOWrVnVobrVneHAqRfnVN3qTtWs5lT1GIfiYizFxTgUF+1QJOEDoJwIBfKk/N0K5e1WKH/fV86fCvm3FoZLOF58Ut7Og6/AcshRvZEcNVrIWaOFnHW7ynJFl/0NAcopYgaArU6KdWpY51gN61x4PHtPQEuy8pS1LaCN2wu/Nmwr0MYdAf3yR4HSN+Qf9TrdkZbiogvjpnqMQ9X2fY+LsRQXvf9w4XF3pEPREZZiIixFu6ToCEtRLksxkZaiXZaiIwq/u5wEElCRhUIhKRSQAnlSILcwQoJ5UiBPoUBu4fd9x1WQsy9O9hSGyr5YUf6efcGyJxwwCh79d5Yiq8qKOUmOhFRZbo+smERZ7pPkqFJbjoSmslwxpX8HAIYiZgCUKwlVnDoz9dB/uEOhkHbuDWnj9oB+216g37YHtGVXUDtygtqxt/D79pyQduYEtT0nqG3+oNZlB7Q3/8Tfi+NyKBw20RFWkcMxEZaiDoihmH1BtH+ZmH3LRx2w/IHHI52S02HJ5ZCcjsLDTuuvww5LcjmLnlbksEP7LvvX6ZZFfMF+oVBQCn8FDjgcVCgYOOi0A4+Hwof/frl9QRHIKwyOfdGxPzj+io7cv5YJ5h/l/H3XEwqe+I12RsuKjJUiq8lRpY6siFgpsoqsiNjCwxFVZMUkyLEvWKwYD7ECnABiBoAxLMsKv5ysee2IY77c3vzQYYPHnxdSbkFIOfkh7c0PaW/Bvu8HHM7JDym3QOHT9y+73R9Uzr7j5W3fBZalIsHz99jZf9jh+FtIWda+84su73JY4WUPFVUuxwGXs/Yve8B1W3+F14GXc1j757VkHTC7JFkHHJalIuf/dfjIl7MsyQoGpGCuFMjf9z1PVihfCgVlKSRLofBhKSSFQopyBJQcvVmWCmcs+hWSte9wtEuKcUlREZZiIqRoV+FtVHiPUwf8wwiFDnFcCumAZcNnH7Bskb1X7T/tSA/+DzgvGNh33qGDQqHAvvMOFxWBEwiSwJH/kdrB4ZIckZIzUpYzSnLFyBFZTXJGSs4oWc7IoufvO245I/ctEynLESm5CoOlME4Kv1uR+0LFwUMroCyxxQGo8AqfSSl8f01pKQgUxk84iv4eRwUqjKJ9p+XkFX7PzQ8pLxBSICgVBAu/B0JS4FCHg4c5fd/hggPOD/7t/IJAaN9y+04/4HB+QUg5R1l3QQn8h3X5ELnv68iaxGzSwrRnin3tgX1fZrIky7Hvyyk5nOHj1v7T9p/vcEmWM3y6Fb7cgcs5D33Z8HU4j3BZR5HrL3q5v8XFvuiwHJGHiZLIv6LEUXq/AwDYg5gBgBLgclqKdVqKjbJ7ktITDB4+iAoOE1rBIqF18OVC+uuJh9ABT0yEQlIwkKuQf4uCe35XaM8WBf1/KLBni0K52/56gmLfcyuFh6WQHJIzSnLGSK5IhRzRkjNackUVnr7veMgZIcmhkOWQZCkkh0KWFT7NZYW0JHKMgkEpGCp8ziYQKjwcDIUUDFoqCIWUmy/586WcvJD25IfkzyuM1j15hcG6J6/w/P3z/X1mhyXVjnPKG+9SUnyEvPFOeRMK9+QX4bQOeGpKCj8fZVn6KzwO9eD/GIPkoOjgpYkAzEPMAACOicNhySEpwin99UKvkhUKBRX4/TsVrPpQgd+/U5GXZUmyanhkVUuWo1p9OfZ9t6LipAi3LFeVwpf/lLMH5cFgSLvzQtq5N6idOSH9vjOglX/k6+ff85Xxe4E+2pyv7LVFn/qKdEr/PNmtf3WL1Sn1K3AhA8AJImYAALYLBfJUsHq68jM/VGj3RkmSo0ZzORJSC6MlrjBerMiqNk9afA6HpWrRlqpFO6TqUvPaETq96V+71g2FQtqyK6iff98fOPman5mrN5b49cYSv06pH6nru8VqUDt3uQs1ALAbMQMAsFUoFFLu4kcVyJojOaPkanC2XI0Hylk9xe7RyoRlWapZrfA9XT2bFEZOKBTSvMxcPffVbn30Q46GvpqtDdsCuuWMajZPCwDlCzEDALBVQeaHCmTNkcPTWtFd7it82VglZ1mWejSOVo/G0VrjK1DfZ7foro936NQGkeqWwgcmAsB+DrsHAABUbqHc7bLiGyuy/RhC5hAaelz6z8Dq6pgcqTVbC+weBwDKFWIGAGCr4I41Cm1bJUdMgt2jlFu1qjm1aF2e1mWbu+NnACgNxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAI7nsHgCoiOLrt1Tyqecqskqc3aMA5Z6jRgvJchZ+4ZDi3Q6d1zZGzWtH2D0KUO5F1qqjuB5nKLJmHbtHQRmwQqFQyO4hAAAAAKC4eJkZAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEjEDAAAAAAjETMAAAAAjETMAAAAADASMQMAAADASMQMAAAAACMRMwAAAACMRMwAAAAAMBIxAwAAAMBIxAwAAAAAIxEzAAAAAIxEzAAAAAAwEjEDAAAAwEj/D9A9qkus/XWDAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 800x1000 with 1 Axes>"
       ]
@@ -188,13 +188,25 @@
     }
    ],
    "source": [
-    "text =  \"As Red Riding Hood walked, a tall shadowy figure appeared among the trees. It was the Big Bad Wolf.\"\n",
+    "text =  \"As Red Riding Hood walked in the forest, \"\\\n",
+    "        \"a tall shadowy figure appeared among the trees. \"\\\n",
+    "        \"It was the Big Bad Wolf.\"\n",
     "reader.text2circuit(text).draw(figsize=(8,10))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6776b3be",
+   "metadata": {},
+   "source": [
+    "Note that the entity \"Red Riding Hood\" has been represented simply as \"hood\", modified by the adjectives \"red\" and \"riding\" later in the timeline of the interactions in the text. Similarly, \"big bad wolf\" was analysed into a \"wolf\", modified by the higher-order boxes \"big\" and \"bad\". While this analysis makes linguistic sense, for cases like these it has very limited use. \n",
+    "\n",
+    "{py:class}`~lambeq.experimental.discocirc.DisCoCircReader` provides the means to avoid using that level of details through _rewrite rules_ that collapse long modification chains into a simple entity. For example, in order to collapse all the noun-modification chains in the above diagram you can use the `noun_modification` rule as below:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "id": "e8fb4ac3",
    "metadata": {},
    "outputs": [
@@ -210,24 +222,35 @@
     }
    ],
    "source": [
-    "text =  \"As Red Riding Hood walked in the forest, a tall shadowy figure appeared among the trees. It was the Big Bad Wolf.\"\n",
-    "reader.text2circuit(text, rewrite_rules=['noun_modification', 'verb_modification']).draw(figsize=(7,7))"
+    "reader.text2circuit(text, rewrite_rules=['noun_modification']).draw(figsize=(7,7))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d0cc7d7a",
+   "id": "24002bf9",
    "metadata": {},
    "source": [
+    "The resulting diagram is significantly simplified, while the entities on the top of the diagram make much more sense for the given story.\n",
+    "\n",
+    "The table below includes all the pre-defined rewrite rules with short descriptions.\n",
+    "\n",
     "(tbl-tree-rewrites)=\n",
     "\n",
     "| Rewrite rule                                 | Description                                                                    |\n",
     "| -------------------------------------------- | ------------------------------------------------------------------------------ |\n",
-    "| `determiner`                                 | Removes determiners (such as \"the\") by replacing them with caps.               |\n",
-    "| `auxiliary`                                  | Removes auxiliary verbs (such as \"do\") by replacing them with caps.            |\n",
-    "| `noun_modification`                          |                                                                                |\n",
-    "| `verb_modification`                          |                                                                                |\n",
-    "| `sentence_modification`                      | Simplifies prepositions by passing through the noun wire using a cap.          |"
+    "| `determiner`                                 | Joins determiners (\"the\", \"an\", \"a\") with the nouns they modify              |\n",
+    "| `auxiliary`                                  | Attempts to collapse auxiliaries (\"do\", \"is\" etc) with entities or actions they modify |\n",
+    "| `noun_modification`                          | Collapses all noun modification chains into a single entity |\n",
+    "| `verb_modification`                          | Attempts to collapse verb modification chains into a single action |\n",
+    "| `sentence_modification`                      | Attempts to collapse sentence modification chains |\n",
+    "\n",
+    "```{note}\n",
+    "Users can create their own rewrite rules programmatically by using the class {py:class}`~lambeq.experimental.discocirc.TreeRewriteRule` in the {py:mod}`lambeq.experimental.discocirc` package.\n",
+    "```\n",
+    "\n",
+    "```{tip}\n",
+    "To collapse _all_ possibile modification chains in a diagram, create an empty {py:class}`~lambeq.experimental.discocirc.TreeRewriteRule` and pass it to `rewrite_rules` argument of the {py:meth}`~lambeq.experimental.discocirc.DisCoCircReader.text2circuit` method.\n",
+    "```"
    ]
   },
   {