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" \"'2024-12-15T17:30:54.037653Z'}}}, 8: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2024-12-15T17:30:54.041189Z', 'iopub.status.busy': \"", " \"'2024-12-15T17:30:54.040902Z', 'iopub.status.idle': '2024-12-15T17:30:5 [\u2026]"], "unified_diff": "@@ -51,18 +51,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.851592Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.848208Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:57.856150Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:57.855013Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.034224Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.033919Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.038539Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.037653Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# 2 empty lines before, 1 after\\n\"\n@@ -76,18 +76,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.867147Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.866123Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:57.893957Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:57.892889Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.041189Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.040902Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.051023Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.050170Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"42\"\n@@ -110,18 +110,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.903467Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.902742Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:57.910254Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:57.909259Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.053695Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.053377Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.058064Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.057129Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -141,18 +141,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.920439Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.919721Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:57.932053Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:57.931165Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.060597Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.060301Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.066296Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.065345Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -185,18 +185,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.939155Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.938479Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:57.955538Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:57.949187Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.068898Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.068615Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.078056Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.077147Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -260,18 +260,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.964997Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.964274Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:57.972101Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:57.971084Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.080943Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.080654Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.087415Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.086596Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n@@ -290,18 +290,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.976140Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.975411Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:57.982299Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:57.981388Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.090050Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.089775Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.095456Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.094660Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n@@ -324,18 +324,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:57.990922Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:57.990458Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:58.003813Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:58.002815Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.098151Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.097875Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:54.107785Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:54.106847Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/svg+xml\": [\n \"<svg xmlns:dc=\\\"http://purl.org/dc/elements/1.1/\\\" xmlns:cc=\\\"http://web.resource.org/cc/\\\" xmlns:rdf=\\\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\\\" xmlns:svg=\\\"http://www.w3.org/2000/svg\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\" 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\"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"<img src=\\\"https://www.python.org/static/img/python-logo-large.png\\\"/>\"\n@@ -452,18 +452,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:58.023280Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:58.022773Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:59.200030Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:59.198844Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:54.118451Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:54.118177Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:55.210538Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:55.209277Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"URLError\",\n \"evalue\": \"<urlopen error [Errno -3] Temporary failure in name resolution>\",\n \"output_type\": \"error\",\n@@ -503,18 +503,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:59.211157Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:59.210676Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:59.217519Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:59.216574Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:55.214180Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:55.213842Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:55.220255Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:55.219349Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"<img src=\\\"https://jupyter.org/assets/homepage/main-logo.svg\\\"/>\"\n@@ -540,18 +540,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:59.222959Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:59.222000Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:59.228898Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:59.228045Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:55.223153Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:55.222860Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:55.229050Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:55.228241Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/latex\": [\n \"$\\\\displaystyle \\\\int\\\\limits_{-\\\\infty}^\\\\infty f(x) \\\\delta(x - x_0) dx = f(x_0)$\"\n@@ -572,18 +572,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:59.233306Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:59.232896Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:59.239039Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:59.238213Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:55.231913Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:55.231293Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:55.236793Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:55.235914Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/latex\": [\n \"$\\\\displaystyle \\\\int\\\\limits_{-\\\\infty}^\\\\infty f(x) \\\\delta(x - x_0) dx = f(x_0)$\"\n@@ -601,18 +601,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:59.246742Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:59.246308Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:59.253520Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:59.252517Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:55.239532Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:55.239236Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:55.244915Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:55.244081Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/latex\": [\n \"This is a \\\\LaTeX{} equation: $a^2 + b^2 = c^2$\"\n@@ -632,18 +632,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:59.261151Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:59.260679Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:44:59.268491Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:44:59.267454Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:55.247523Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:55.247242Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:55.264666Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:55.263766Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/latex\": [\n \"\\\\begin{equation}\\n\",\n@@ -733,34 +733,34 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:44:59.277058Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:44:59.276284Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:00.163063Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:00.162004Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:55.268201Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:55.267568Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:56.049229Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:56.048142Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:00.170931Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:00.170319Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:00.792581Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:00.791243Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:56.052840Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:56.052414Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:56.533689Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:56.532612Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n 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\"2026-01-17T23:45:00.819890Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:00.811827Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:56.537354Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:56.536648Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:56.546808Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:56.545723Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%config InlineBackend.figure_formats = ['png']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:00.824183Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:00.823663Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:00.977737Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:00.976599Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:56.550349Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:56.549945Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:56.693028Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:56.691900Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n@@ -1316,34 +1316,34 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:00.982076Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:00.981011Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:00.992578Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:00.989073Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:56.698704Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:56.698229Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:56.709252Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:56.707913Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%config InlineBackend.figure_formats = ['png2x']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:00.997269Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:00.996442Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:01.178267Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:01.177417Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:56.713188Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:56.711920Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:56.897835Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:56.890067Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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AAAAB+pqSqTjuPGXePJ6QneuzruZ53zcTwwERyDQAAAMCvbMgpUpN2aw3pEaNu0WEe+3rsXEMiuQYAAADgZ9xKwj3Ub91geO9YhQY1plZHi6uUX1rt0a8J70NyDQAAAMCvuA4z88T51k2FBQdpeO9Yw1oWu9cBh+QaAAAAgN84VVGr3cdLndcWi2f7rRtQGg6SawAAAAB+Y32Ocdd6WM9YxUeGevzrug4128pQs4BDcg0AAADAb3R1v3WDjL4JhuuvjxTL1nSqGvweyTUAAAAAv+Habz3Rw/3WDfomRigxqnGHvKLWpv35ZV3yteEdSK4BAAAA+IXC8hrtyyt3Xlst0vld0G8tSRaLRRmufdeUhgcUkmsAAAAAfmGdy671OX3iFBse0mVf33WoWRbJdUAhuQYAAADgF9z6rbuoJLzBKNehZkwMDygk1wAAAAD8gtv51l00zKzBeX3jZbE0Xu/LL1NZdV2XxgDzkFwDAAAA8Hl5pdXKLqhwXgdZLRqX1jX91g1iw0M0oHu089rhkLYfKenSGGAekmsAAAAAPs+133pkSpyiw4K7PA63vmtKwwMGyTUAAAAAn2d2v3WDDJe+a4aaBQ6SawAAAAA+z63f2qTk2nXneuvhYjkcDlNiQdfySHKdm5sri8XSpo9Nmza1+l6rVq1SZmamevfurfDwcKWmpuq2227T+vXrPRE6AAAAAB9zrLhKB09WOq9Dgiwam5ZgSixDesQoIiTIeV1YXqOjxVWmxIKu5dU71w899JCuvPJKLVu2TMePH1dNTY0OHz6spUuXatKkSXr22WfNDhEAAACAyVz7rc9LiVdkaNf3W0tScJBV56bEGdYoDQ8MHk+uV6xYobKyshY/Ro8e3exzzz//vBYsWCBJmjp1qjZs2KCCggJ99tlnmjBhgmw2m2bPnq133nnH038EAAAAAF7Mrd+6i4/gcpXRTGk4/J/Hf50TERGh6OjoM9/YRGFhoebOnStJuuSSS7Rs2TJZrad/DzB58mStXr1aGRkZ2rt3rx555BFdffXVCg0N7ezQAQAAAPgA135rs4aZNWiu7xr+zyvLwpcsWaKysjJJ0vz5852JdYOIiAjNmzdP0un+7hUrVnR5jAAAAADMd7ioUkdONfY0hwZZNbqfOf3WDTJSjV9/+9ES1dbbTYoGXcUrk+tly5ZJkvr3769x48Y1e09mZqbCw8MlSe+//36XxQYAAADAe7juWmekxiu8yUAxM/SMC1fP2HDndW29XXtOlJoYEbpClyXXtbW1bb53y5YtkqSJEye2eE9YWJgyMjIkSZs3b+5YcAAAAAB80jov67duQGl44PF4cv2Tn/xEMTExCgsLU1hYmEaMGKGHHnpIBw4caPb+o0ePOkvC09PTW33vhtf37dt3VmfHnTp1qsWP4uLidr8fAAAAgK7jcDi8rt+6QUZqvOGaieH+z+MDzXbu3On8vLa2Vrt27dKuXbv08ssv69lnn9UDDzxguL+wsND5eY8ePVp97+TkZElSTU2NysvLFRMT067YEhMT23U/AAAAAO9x8GSljpdUO6/Dgq0a5ZLUmoWd68DjkZ1rq9WqK664Qq+//rq2bdumkydPqrq6Wnv27NFvf/tbxcbGqra2Vg8++KAWLlxoeLaiosL5eUNPdUsiIiKcn5eXl3fuHwIAAACAV3PdtR6blqCwYHP7rRucmxKnIKvFeZ1TWKFTFW1vlYXv8cjOdWpqqj788EO39SFDhuixxx7TtddeqwsvvFAnT57Uo48+qhtuuEGxsbGSZCjvtlgsbu/RmYqKilp8rbi4+Ixl6QAAAADM43a+tZeUhEtSZGiwhvSI0a7jjYPMth4p1iVDkk2MCp5kyrTwoUOH6oknnpB0OsFtepRW0zOxq6qq3J5tqunr7T1LW5ISEhJa/IiPj2/3+wEAAADoGs32W3vJMLMGriXqW+m79mumHcV17bXXOj9vmA4uSUlJSc7P8/LyWn2P/Px8Sacnh59Ncg0AAADAN31TUKGCshrndWRokEamxJsXUDPouw4spiXXDcPIJBkmc/fp08c5mCw7O7vV98jJyZEkDR482OMl5AAAAAC8h3u/daJCgkxLb5o12nXn+nDxWZ1yBN9g2nffiRMnnJ8nJCQYXhs9erQkad26dS0+X1NT49zxHjNmjAciBAAAAOCt3M639qJ+6wbpSdGKCW8cc1VSVaecwopWnoAvMy25fuedd5yfNyTTDaZNmybp9M71pk2bmn1+2bJlqq4+PXY/MzPTQ1ECAAAA8DYOh0PrvLzfWpKsVgul4QHEI8n1kSNHWn19+/btmjt3rqTTZ01/73vfM7w+ffp0Z2n4Y489Jrvdbni9urpac+bMkSSlpaXpqquu6qTIAQAAAHi7fXnlOtnkWKvosGCd0zvWxIha5ppcZzHUzG95JLkeNWqUrr/+ei1evFjbt29XYWGhioqKtGXLFv3617/WxIkTnX3WL7zwgvMYrgZJSUnO5PuTTz5RZmamNm3apMLCQn3xxRe69NJLtXv3bknSc889p9DQUE/8MQAAAAB4obXfFBquz++fqGAv67duwM514PDIOdf19fV699139e6777Z4T2RkpH7/+99r+vTpzb7+8MMPKzc3Vy+++KKWL1+u5cuXG163Wq2aP3++rr/++k6NHQAAAIB3czuCywv7rRu4Jte7j5equs6m8JAgcwKCx3gkuV64cKH++9//av369Tpy5IhOnjypmpoaxcfHa9iwYbr88st11113qVevXq2+z4IFCzR16lS99NJL2rBhg4qKipScnKyLLrpIDz30kMaPH++J8AEAAAB4KbvdofU5RYY1b+y3btAtOkypiZE6VFQpSaq3O7TjaInGpiWaHBk6m0eS6+uuu07XXXddp7zXlClTNGXKlE55LwAAAAC+bfeJUhVX1jmvY8ODNayXd/ZbNxjVN96ZXEunS8NJrv2PdzYmAAAAAEAz1rocwTU+vZuCrBaTommbDJfzrhlq5p9IrgEAAAD4DLcjuLy437oBQ80CA8k1AAAAAJ9g87F+6wbDe8cqtMk086PFVcovrTYxIngCyTUAAAAAn7DzWInKquud1wmRIRrSI8bEiNomLDhIw13O4c5i99rvkFwDAAAA8Amu/dYT0rvJ6uX91g0oDfd/JNcAAAAAfILb+dY+UBLewHWo2VaGmvkdkmsAAAAAXq/OZtdG135rHxhm1iCjb4Lh+usjxbLZHSZFA08guQYAAADg9bYfLVFFrc15nRQdpoHJ0SZG1D59EyOUGBXqvK6otWl/fpmJEaGzkVwDAAAA8Hru/daJslh8o99akiwWi3vfNaXhfoXkGgAAAIDXczvf2of6rRtkuCTXWSTXfoXkGgAAAIBXq623a1PuKcOaL/VbNxjlOtSMieF+heQaAAAAgFfbdqRYVXWN/dY9YsPUPynKxIjOzsiUeMP1vvwyldfUN38zfA7JNQAAAACv5tpvPTG9m0/1WzeIiwgxDGFzOKSv2b32GyTXAAAAALyaW3Ltg/3WDVyHmmWRXPsNkmsAAAAAXqu6zqbNh1z7rZNMiqbj3CaGk1z7DZJrAAAAAF4r61Cxauvtzus+8RHqmxhhYkQdk+Ey1CzrULEcDoc5waBTkVwDAAAA8Fprs13Pt/bNfusGQ3rEKCIkyHldWF6jo8VVJkaEzkJyDQAAAMBrrfOjfmtJCg6y6tyUOMMa5137B5JrAAAAAF6pqtamrMMu/dY+nlxLUgZ9136J5BoAAACAV9p88JTqbI39yKmJkeoT77v91g0YauafSK4BAAAAeKW12YWG6wnpiSZF0rkyUhMM19uPlhiGtsE3kVwDAAAA8Er+dL51Uz3jwtUzNtx5XVtv154TpSZGhM5Acg0AAADA61TU1OvrIyWGNV8+39oVpeH+h+QaAAAAgNfZmFukentjv3X/pCj1jAtv5Qnf0tx51/BtJNcAAAAAvM667CLD9YR0/ygJb8DOtf8huQYAAADgddZm+2e/dYNzU+IUZLU4r3MKK3SqotbEiNBRJNcAAAAAvEpZdZ12HDX2W/vLpPAGkaHBGtIjxrC29UixOcGgU5BcAwAAAPAqG3OLZGvSbz0wOVrJMf7Tb91glEvf9Vb6rn0ayTUAAAAAr+J2BJef9Vs3oO/av5BcAwAAAPAq/t5v3WC068714WI5HI7mb4bXI7kGAAAA4DVKKuu081ipYc3fJoU3SE+KVkx4sPO6pKpOOYUVJkaEjiC5BgAAAOA11uecVNPN26E9Y5QYFWpeQB5ktVp0Xkq8YY3ScN9Fcg0AAADAa7iWhPvrrnWDDJfS8CyGmvkskmsAAAAAXsNtmJmf9ls3YKiZ/yC5BgAAAOAViipqtedEmfPaYpEm9A+s5Hr38VJV19nMCQYdQnINAAAAwCusdykJH94rVnGRISZF0zW6RYcpNTHSeV1vd2jH0RITI8LZIrkGAAAA4BXcjuDy837rBpSG+weSawAAAABeIdD6rRu4JtcMNfNNJNcAAAAATFdQVqP9+eXOa6tFGtc/0cSIuo7rxHB2rn0TyTUAAAAA061zKQk/t0+cYsP9u9+6wfDesQoNakzNjhZXKb+02sSIcDZIrgEAAACYzu186wApCZeksOAgDe8da1jLYvfa55BcAwAAADDdOtd+6wAZZtaAoWa+j+QaAAAAgKnySquVXVjhvA62WjQuLTD6rRu49V0z1MznkFwDAAAAMJXrlPCRKXGKCgs2KRpzZPRNMFx/faRYNrvDpGhwNkiuAQAAAJgqUI/gaqpvYoQSo0Kd1xW1Nu3PLzMxIrQXyTUAAAAAU7kOM5uYnmRSJOaxWCzufdeUhvsUkmsAAAAApjlaXKVDRZXO65Agi8b0S2jlCf+V4ZJcZ5Fc+xSSawAAAACmcS0Jz+iboIjQIJOiMdco16FmTAz3KV2aXBcUFCgpKUkWi0UWi0UzZ8484zOrVq1SZmamevfurfDwcKWmpuq2227T+vXrPR8wAAAAAI9yTa4D6XxrVyNT4g3X+/LLVF5Tb04waLcuTa5/+tOf6uTJk2e+8VsPPfSQrrzySi1btkzHjx9XTU2NDh8+rKVLl2rSpEl69tlnPRgtAAAAAE9yOBxa59ZvHbjJdVxEiAYmRzuvHQ7pa3avfUaXJdcrV67U0qVLlZ6e3qb7n3/+eS1YsECSNHXqVG3YsEEFBQX67LPPNGHCBNlsNs2ePVvvvPOOJ8MGAAAA4CGHi6p0tLjKeR0abHU77znQuA41yyK59hldklxXVFToRz/6kSTppZdeOuP9hYWFmjt3riTpkksu0bJlyzRu3DglJSVp8uTJWr16tYYMGSJJeuSRR1RbW+ux2AEAAAB4xtrsQsP1mNQEhYcEZr91A7eJ4STXPqNLkuvHH39cubm5uvnmmzVlypQz3r9kyRKVlZ0+023+/PmyWo1hRkREaN68eZKk3NxcrVixovODBgAAAOBRnG/tznXnPutQsRwOhznBoF08nlxv3LhRL774ouLi4vTCCy+06Zlly5ZJkvr3769x48Y1e09mZqbCw8MlSe+//37nBAsAAACgSzgcDvfzrUmuNaRHjCKa7N4XltcYSufhvTyaXNfX1+vuu++WzWbTU089pV69erXpuS1btkiSJk6c2OI9YWFhysjIkCRt3ry548ECAAAA6DI5hRXKK61xXkeEBOk8l2nZgSg4yKpz+8QZ1igN9w0eTa6feeYZbdu2TePHj9d9993XpmeOHj3qLAk/0/Czhtf37dtHqQQAAADgQ1x3rcemJSg0uEsPM/JazZWGw/sFe+qNDxw4oHnz5ikoKEgvv/yyW990SwoLG4ca9OjRo9V7k5OTJUk1NTUqLy9XTExMu2I8depUi68VFxe3670AAAAAtJ3b+dYBfASXK4aa+SaPJdf33HOPqqur9cgjj2jUqFFtfq6iosL5eUNPdUsiIiKcn59Ncp2YmNiu+wEAAAB03OnzrYsMa/RbNxrlsnO9/WiJauvt7Ox7OY/8r/P666/r008/VWpqqp544ol2Pdu0vNtisXR2aAAAAABMdiC/XIXljf3WUaFBbn3GgaxXXIR6xjZuNNbW27XnRKmJEaEtOn3nOi8vT7Nnz5Ykvfjii4qKimrX89HR0c7Pq6pan4rX9PWmz7VVUVFRi68VFxefsecbAAAAQPu59luP65+okCB2ZZsa1TdeK3eecF5vPVyskQx882qdnlz/4he/0KlTp3Tttddq2rRp7X4+KSnJ+XleXl6r9+bn50s6PTn8bJLrhISEdj8DAAAAoGPczrem39rNqFRjcp11qFjTWz5MCV6g0389lJ2dLUl67733ZLFYmv1osHjxYufaokWLJEl9+vRx9k43vFdLcnJyJEmDBw+mhBwAAADwAXa7Q+s43/qMMhhq5nO8svZi9OjRkqR169a1eE9NTY3zPOwxY8Z0SVwAAAAAOmZvXplOVdY5r2PCgjWiN/3Wrs5NiVOQtXEDMaewQqcqak2MCGfS6WXhr732msrLy1u9JyMjQ5J0zTXXaN68eZKk1NRU5+vTpk3T559/ruzsbG3atEljx451e49ly5apurpakpSZmdlZ4QMAAADwINeS8PP7JxqSSJwWGRqsIT1itOt44yCzrUeKdcmQZBOjQms6PbkeOHBgm+9NTExs9piu6dOna+7cuSorK9Njjz2mDz/80HBOdnV1tebMmSNJSktL01VXXdXhuAEAAAB4nuswM0rCWzYqNd6YXB8iufZmXlkWnpSUpLlz50qSPvnkE2VmZmrTpk0qLCzUF198oUsvvVS7d++WJD333HMKDQ01MVoAAAAAbWGzO7TeJbmewDCzFo2i79qndPrOdWd5+OGHlZubqxdffFHLly/X8uXLDa9brVbNnz9f119/vUkRAgAAAGiP3cdLVVpd77yOiwjR8F6xJkbk3Uanxhuutx4ulsPhYJizl/LKnesGCxYs0MqVKzVt2jT17NlToaGhSklJ0S233KI1a9bo0UcfNTtEAAAAAG3k2m89vn+irPRbtyg9KVox4Y37oSVVdcoprDAxIrTGlJ1rh8PR5nunTJmiKVOmeDAaAAAAAF2BI7jax2q16LyUeP33QKFzbevhYqV3jzYxKrTEq3euAQAAAPiHeptdG3KKDGsk12eW4VIannWo2JQ4cGYk1wAAAAA8buexUpXVNPZbJ0aFanByjIkR+QaGmvkOkmsAAAAAHud6BNeEdPqt28I1ud59vFTVdTZzgkGrSK4BAAAAeJzrMLOJHMHVJt2iw5SaGOm8rrc7tONoiYkRoSUk1wAAAAA8qs5m18Zc+q3PFqXhvoHkGgAAAIBHfX2kRJW1jaXM3WPCNICJ123mmlxnkVx7JZJrAAAAAB7legTXhPRusljot24r14nhW5kY7pVIrgEAAAB4FP3WHTO8d6xCgxpTt6PFVcovrTYxIjSH5BoAAACAx9TU27TpIP3WHREWHKRhvWMNa5SGex+SawAAAAAes+1wiarr7M7rnrHhSusW2coTaE4GQ828Hsk1AAAAAI9xKwkfQL/12aDv2vuRXAMAAADwmLXZhYZr+q3PjuvE8K+PFMtmd5gTDJpFcg0AAADAI6rrbNrissNKv/XZSU2MVGJUqPO6otam/fllJkYEVyTXAAAAADxiy6FTqq1v7LfuEx+hvon0W58Ni8XitntNabh3IbkGAAAA4BHrmum3xtlzHWqWRXLtVUiuAQAAAHjE2mzOt+5Mo1yHmjEx3KuQXAMAAADodFW1Nrfkj53rjhmZEm+43pdfpvKaenOCgRuSawAAAACdbtPBItXZGqdZ9+sWqd7xESZG5PviIkI0MDnaee1wSF+ze+01SK4BAAAAdDq3860pCe8UrkPNskiuvQbJNQAAAIBO59ZvTUl4p3CbGE5y7TVIrgEAAAB0qvKaen19pMSwxs5158hwGWqWdahYDoej+ZvRpUiuAQAAAHSqjblFstkbE7707lFKjg03MSL/MaRHjCJCgpzXheU1OlpcZWJEaEByDQAAAKBTuZ1vza51pwkOsurcPnGGNUrDvQPJNQAAAIBORb+1ZzVXGg7zkVwDAAAA6DSl1XXacdTYbz2BnetOxVAz70RyDQAAAKDTbMguUpN2aw3uEa2k6DDzAvJDo1x2rnccLVFtvd2cYOBEcg0AAACg07iVhLNr3el6xUWoZ5MBcTX1du05UWpiRJBIrgEAAAB0orWuw8zot/YISsO9D8k1AAAAgE5RXFmr3U12UC0WaXx/kmtPcC0N38pQM9ORXAMAAADoFOuyi+Ro0m89tGesEqJCzQvIj2W47FxnsXNtOpJrAAAAAJ1iHf3WXebclDgFWS3O65zCCp2qqDUxIpBcAwAAAOgU9Ft3ncjQYA3uEWNY23qk2JxgIInkGgAAAEAnOFleo715Zc5rq0U6v3+iiRH5vwz6rr0KyTUAAACADluXXWS4HtE7TnERISZFExiYGO5dSK4BAAAAdNja7ELDNSXhnuc61Gzr4WI5mk6UQ5ciuQYAALLbHVq6/pCeWrFb+5qUdQJAW7n1WzPMzOMGdI9WTFiw87qkqk45hRUmRhTYSK4BAID+/Pk3+p93t+svX2TrppfX6mhxldkhAfAh+aXV+qagMakLslo0Ni3BxIgCg9Vq0XmUhnsNkmsAAAJcZW29Xvn8G+d1SVWdfrtit4kRAfA1a12O4DqnT5xiwum37gquQ82yGGpmGpJrAAAC3Dtbjqq0ut6wtvzr41rv8sMyALSE863Nw1Az70FyDQBAAHM4HFq0JrfZ1+b+e5dsdgbjADgzzrc2j2tyvft4qarrbOYEE+BIrgEACGD/PVCoA/nlzb62+3ip3t5wqIsjAuBrjpdUKfdkpfM62GrR2H70W3eVbtFhSk2MdF7X2x3acbTExIgCF8k1AAABbOFXuYbrYKvFcP3ch3tVXFnbhREB8DWuu9bn9Y1XVJMJ1vA8SsO9A8k1AAABKqewQqv35BvW/vCDDEWEBDmvT1XW6YWP9nV1aAB8CEdwmc81uc4iuTYFyTUAAAFqsUuv9ciUOF11bk/95NKBhvW31h/SnhOlXRgZAF+yLod+a7O5TgzfysRwU5BcAwAQgMqq6/SvzUcMazMvSJPFYtGdF/Y39O/Z7A49sWyXHA6GmwEwOnKqUoeLqpzXoUFWjaHfussN7x2r0KDG1O5ocZXyy6pNjCgwkVwDABCA/rX5iMprGo/fSooO09SRvSRJ4SFBenzqMMP9a7NPauWOE10aIwDv51oSPio1XuFNWkvQNcKCgzSsd6xhjd3rrkdyDQBAgLHbHW4l4T+ckKqw4MYfiK8Y3kMXDUoy3POb/+zmeBcABms539prZNB3bTqSawAAAsyne/MNx+aEBFl06/hUwz0Wi0VzrhlumB5+tLhKr3ye3WVxAvBuDodD6zjf2mvQd20+kmsAAAKM6/Fb14zsreSYcLf7BibHaPrENMPanz8/oKPFVW73Agg8h4oqdayksa83LNjqluCh67hODP/6SLFsdmZldCWPJNdHjhzRn//8Z915550aN26cUlNTFRERocjISA0YMEC33HKLPvjggza/36pVq5SZmanevXsrPDxcqampuu2227R+/XpPhA8AgN/an1em/x4oNKzNmtS/xfsfunyQukWFOq+r6+x6asVuj8UHwHe49luP6ZdgaC9B10pNjFRik7+vK2pt2p9fZmJEgccjyfXy5cv14x//WG+88YY2bdqkw4cPq7q6WlVVVcrOztbf/vY3XXXVVcrMzFRlZWWr7/XQQw/pyiuv1LJly3T8+HHV1NTo8OHDWrp0qSZNmqRnn33WE38EAAD80kKXXuux/RJ0bkpci/fHRYRo9pQhhrX/fH1c61z6LAEEHvqtvYvFYnHbvaY0vGt5JLkODw/XlClT9PTTT+ujjz7Szp07VVhYqH379umdd97RRRddJElatmyZ7rrrrhbf5/nnn9eCBQskSVOnTtWGDRtUUFCgzz77TBMmTJDNZtPs2bP1zjvveOKPAQCAXymurNU7W1yO35qUdsbnbhrbV+f0MU6hnbtsp+pt9s4MD4APcTgcbjvX9FubzzW5ziK57lIWhwmHVjocDl199dVasWKFJCknJ0dpaWmGewoLC5Wenq6ysjJdcskl+vjjj2W1Nv4uoKqqShkZGdq7d6/S0tK0d+9ehYaGqrOcOnVKiYmJkqSioiIlJHBeHwDAt73y+Tf67Qd7nNe94sL1xc8uUUjQmX/Xvim3SDe+vNaw9uS15+j2Cf06PU4A3u+bgnJd9tznzuuIkCBtm/NdhQYz0slMX+4v0O2vb3BeD+kRo1X/7zsmRuTdOjvnM+W732Kx6M4773Reb9682e2eJUuWqKzsdI/A/PnzDYm1JEVERGjevHmSpNzcXGeiDgAA3NXb7Fqy9qBh7faJ/dqUWEvS2LREXTuqt2HtuQ/3qriyttNiBOA7XHetx6YlkFh7gZEp8YbrffllKq+pNyeYAGTafwEhISHOz8PD3SeULlu2TJLUv39/jRs3rtn3yMzMdD77/vvveyBKAAD8w0e78gxTvsOCrbplXGorT7h77HvDFBnaOKyouLJOz3+0r9NiBOA73PqtKQn3CnERIRrQPcp57XBIX3PedZcxLbn+29/+Jul0kp2RkeH2+pYtWyRJEydObPE9wsLCnM82t/sNAABOcx1kdl1GHyVEta+dqmdcuO6/ZKBh7a11B7X7eGlHwwPgQxwOh9YzzMxrZaQaS5uzSK67TJcm1wUFBfryyy910003aenSpZKkn/70p+rd21hmdvToUWdJeHp6eqvv2fD6vn371N728VOnTrX4UVxc3K73AgDAW+08VqINOUWGtbYMMmvOnRf2V79ukc5ru0N64t872/1vMADftT+/XIXljS0h0WHBOrdPy6cOoGu5TQwnue4ywZ7+AjNnztTixYvd1uPj4zV79mz94he/cHutsLDx/M0ePXq0+v7JycmSpJqaGpWXlysmJqbNsTU0rwMA4M8WfpVruJ6Y3k1De8Y2f/MZhIcE6fGpw3X3kk3OtXXZRVqx/YSmjuzVkTAB+AjXfutxaQkKbuP8BnhecxPDHQ6HLBaLOQEFEFP+K7Barbrjjjt08803N/s/ckVFhfPz5vqxm4qIiHB+Xl5e3nlBAgDgBwrLa7Rs6zHD2qyz3LVucPmwZF00KMmw9tSK3aqqtXXofQH4Bo7g8m5De8YoPKQxzSssrzHM3IDneDy5fuWVV1RWVqbS0lIdPnxY77//vi699FI9//zzGjlypP7xj3+4PdO0tMyTv2EpKipq8SM7O9tjXxcAgK7y9vpDqm1yHnXfxAhdNqz1qrAzsVgsmnPNcAVbG/+NPlpcpVe++KZD7wvA+9ntDq3Lce23TmrhbpghOMiqkX3iDWuUhncNjyfXYWFhio6OVkxMjFJSUjRt2jR99NFHuu+++1RZWalbb71VmzZtMjwTHR3t/LyqqvXfsjR9velzbZGQkNDiR3x8fLveCwAAb1Nbb9eb64zHb82YmKYga8d/cT0wOUYzLkgzrP35s2905FRlh98bgPfac6JMxZV1zuvY8GAN7312bSbwnIzUeMN11qFiU+IINKY1RzzzzDOKjIyUzWbTH/7wB8NrSUmNv/3Ky8tr9X3y8/MlNSbxAADgtA92HFd+WY3zOjI0SDeN7dtp7//Q5YOUFN04cbym3q7frtjTae8PwPu4HsF1fv9unfILO3QuhpqZw7TkOjo6WiNGjJAkZWVlGV7r06ePczDZmcqzc3JyJEmDBw+mSR8AgCZcB5ndOCZFcREhnfb+seEhmj1liGHtP9uPa803hS08AcDX0W/tG0a57FzvOFqi2np78zej05g61q++vr7F10aPHi1JWrduXYv31NTUOM/DHjNmTOcGBwCAD8s6dMptp8K1jLsz3DSmr0amGI/gmffvXaq38UMc4G9sdofWu/Vbk1x7o15xEeoZ2zgYuqberj0nSk2MKDCYllyfPHlSO3bskCQNGDDA7fVp06ZJOr1z7dqT3WDZsmWqrq6WJGVmZnooUgAAfI/rrvXkwd01oHvnt09ZrRbNuWaEYW3PiTIt3XCo078WAHPtOlaqsurGzbGEyBAN7dn2Y3DRtSgN73oeSa53797d6us2m03333+/6upOD0O46aab3O6ZPn26szT8sccek91u/A14dXW15syZI0lKS0vTVVdd1RmhAwDg8/JKq7Vi+3HDWkeP32rNmH4Juj6jj2HtuQ/36VRFrce+JoCutzbb2PIxvn83Wem39lqupeFbGWrmcR5Jrs855xxdc801euONN7Rt2zbl5+eruLhY+/bt0+LFizV+/Hj9/e9/lyRNnjxZt956q9t7JCUlae7cuZKkTz75RJmZmdq0aZMKCwv1xRdf6NJLL3Um8c8995xCQ0Pd3gMAgED01rqDqrc3HmuZ3j1K3xnU3aNf8+ffG6rI0CDndUlVnZ77aK9HvyaArkW/tW/JcNm5zmLn2uOCPfGmdrtdy5cv1/Lly1u978Ybb9TChQtltTaf4z/88MPKzc3Viy++2Oz7Wa1WzZ8/X9dff32nxQ4AgC+rrrNp6XpjSfbMC9I8vrvUIzZcP7l0oJ5e2ZhQL11/SLee349jegA/UG+za2PuKcMaybV3OzclTkFWi2zf/rI1p7BCxZW1io9kU9JTPLJz/eWXX2revHm64oorNGjQIMXFxSk4OFiJiYkaM2aM7r//fq1du1b//Oc/z3h81oIFC7Ry5UpNmzZNPXv2VGhoqFJSUnTLLbdozZo1evTRRz3xRwAAwCf9e9sxnWxSjh0THqwbRqd0yde+88L+6tct0nltd0hz/71TDoejlacA+ILtR0tUXtPYb50UHapByRyD680iQ4M1uIexJ56+a8/yyM71hRdeqAsvvLDT3m/KlCmaMmVKp70fAAD+yOFwuA0yu3lsX0WFeeSfezdhwUH61dThumtJ4yDSDTlF+s/247p6ZO8uiQGAZ7iebz0+vRvH4PqAjNR47T7eOCU861CxLh6SbGJE/s3Uo7gAAEDn2ZBTpF1NfoiyWDxz/FZrLhuWrMmDjf3dT/1nt6pqbV0aB4DO5dZvzRFcPoGJ4V2L5BoAAD/humt9+bAe6psY2fzNHmKxWPSrq4cruEmP97GSav3582+6NA4Anae23q5N9Fv7JNehZlsPF9Oq40Ek1wAA+IEjpyr14a4ThjVPHr/VmoHJ0W5f+5XPv9HhokpT4gHQMV8fKVZVXWP1SXJMmNKTokyMCG01oHu0Ypq0BpVU1SmnsMLEiPwbyTUAAH7gzbUH1eT0LQ3tGWNq2eYDlw1SUnTjRNqaerueWrHbtHgAnL3mjuCi39o3WK0WnUdpeJchuQYAwMdV1tbr7Q3ux2+Z+cNvbHiIfnblUMPaBztOaM2BQpMiAnC2XIeZ0W/tW1z7rrMOFZsSRyAguQYAwMe9m3VUpdWNR+QkRIbo2ow+JkZ02o2jU3ReSpxh7Yl/71K9zW5SRADaq6beps0H6bf2ZRmp8YZrdq49h+QaAAAf5nA4tMhlkNkt56cqPCTInICasFotmjNthGFtb16Z/rr+UAtPAPA2WYeKVVPf+Aux3nHhSu3iQYnoGNed693HS1VdxwkOnkByDQCAD/vvgULtzy93XgdZLfrhhH4mRmQ0OjVB14827qI/9+FeFVXUmhQRgPZw7beeQL+1z+kWHaa+iRHO63q7QzuOlpgYkf8iuQYAwIe57lpfeU5P9Y6PaP5mkzx25VBFhTbupJdW1+u5D/eaGBGAtqLf2j9k9E0wXFMa7hkk1wAA+Kjcwgqt3ptvWLvDpOO3WpMcG64HLhtkWHt7wyHtPMbOCeDNquts2uoy/GoCybVPchtqRnLtESTXAAD4qEVrcuVocvzWyJQ4jU5NaPkBE82alKa0bo19mnaH9MSyXXI0/QMA8CqbD55SbZMBhCkJEepLv7VPGuU61IyJ4R5Bcg0AgA8qq67TvzYfMayZffxWa8KCg/Tra4Yb1jbkFmn518dNigjAmbidb82utc8a0TtWoUGNqd/R4irll1WbGJF/IrkGAMAH/WvzEZXXNB6/lRQdpqkje5kY0ZldOrSHLh7S3bD21Irdqqytb+EJAGZy67fmCC6fFRYcpGG9Yw1r7F53PpJrAAB8jN3u0OI1uYa1H05IVViw+cdvncmvrh6ukKDG3fXjJdV6+bNvTIwIQHMqauq1zaUvl+Tat2XQd+1xJNcAAPiYT/fmK/dkpfM6JMiiW8enmhhR2w3oHq1Zk/ob1l7+IluHiypbeAKAGTYdPKV6e+NMhLRukeoV510nEaB9Mui79jiSawAAfMwil13ra0b2VnJMuDnBnIUHLh2opOgw53VtvV3/+5/dJkYEwJVbvzW71j7PdWL410eKZbMzVLIzkVwDAOBD9ueV6cv9hYY1151gbxcTHqKfXznEsLZy5wl9daCwhScAdDXXfmuO4PJ9qYmRSowKdV5X1Nq0P7/MxIj8D8k1AAA+ZKHLrvWYfgk6NyXOnGA64IbRKTrPZRfliX/vVH2TY38AmKOsuk47jhrPoWdSuO+zWCxuu9eUhncukmsAAHxESWWd3tliPH5r1qQ0c4LpIKvVorkuR3PtyyvXW+sOmhQRgAabck8ZyoUHdI9ScqzvtJ6gZW7JNUPNOhXJNQAAPuJvGw+puq5xZ7dXXLimjOhpYkQdk5GaoBtGpxjWnv9on06W15gUEQCJI7j8metQsyx2rjsVyTUAAD6g3mbXkrXGXd3bJ/ZTSJBv/1P+8yuHKDos2HldWl2v5z7aZ2JEANyGmaUnmRQJOtvIlHjD9b78MpXX1JsTjB/y7X+RAQAIEB/vztPR4irndViwVbeM843jt1qTHBuuBy4daFh7e8Mht35PAF2jpKpOO48Z//ubkJ5oUjTobHERIRrQPcp57XBIX1Ma3mlIrgEA8AFvfJVruL4uo48Smkx99WWzJvVXepLxh70n/r1TDgdHxABdbUNOkZqezjSkR4y6NTk6D74vIzXBcJ1Fct1pSK4BAPByO4+VaENOkWFtpo8OMmtOaLBVv7raONxsY+4pLdt2zKSIgMDF+db+j6FmnkNyDQCAl1vosms9Mb2bhvaMNScYD7lkaLIuGdLdsPbbFXtUWUsvINCVON/a/7km11mHiqkU6iQk1wAAeLHC8hot22rcwfXV47fO5FdXD1dIkMV5faK0Wi99+o2JEQGB5VRFrXYfL3VeWyz0W/ujoT1jFB7SmAYWltcYZnrg7JFcAwDgxd5ef0i1tsbjt/omRuiyYT1MjMhz0rtH645J/Q1rf/kyW4dOVpoUERBY1ucYd62H9YxVfKR/zHZAo+Agq0b2iTesURreOUiuAQDwUrX1dr25znj81oyJaQqyWlp4wvf95NKB6h7TODyptt6u/12xy8SIgMBBv3XgGMV51x5Bcg0AgJf6YMdx5ZfVOK8jQ4N009i+JkbkeTHhIfr5lUMNa6t25um/+wtNiggIHK791hPpt/ZbGQw18wiSawAAvJTrILMbx6QoLiLEnGC60PUZfdwG7jzx752qa1IeD6BzFZbXaF9eufPaapHOp9/ab7nuXO84WqLaev6O7SiSawAAvFDWoVNuOwkzLkgzJZauZrVaNHfaCMPa/vxyvbn2YAtPAOiodS671uf0iVNsuP//Mi9Q9YqLUI/Yxhacmnq79pwobeUJtAXJNQAAXmjRmlzD9eTB3TWge7Q5wZhgVN943TQmxbD2wsf7dLK8poUnAHSEW781JeF+L6NvguGa0vCOI7kGAMDL5JVW6z9fHzes+evxW62ZfeUQRYcFO6/Lquv17Id7TYwI8F9u51szzMzvuZaGb2WoWYeRXAMA4GXeWndQ9XaH8zq9e5S+M6i7iRGZIzkmXA9eNtCw9reNh7XjaIlJEQH+Ka+0WtkFFc7rIKtF49Lot/Z3rrMtsti57jCSawAAvEh1nU1L1x8yrM28IE1WPz5+qzUzL+iv9KQo57XDIc1dtlMOh6OVp+DrqutsZocQUFz7rUemxBmqRuCfRqbEqek/LTmFFSqurDUvID9Acg0AgBf597ZjOlnR+MNNTHiwbhid0soT/i002KpfXTPcsLbp4Ckt23bMpIjgSYXlNbpz0UYN+/VKffeFz/XJ7jyzQwoI9FsHpsjQYA3pGWtYo++6Y0iuAQDwEg6Hw+34rZvH9lVUgO8gXTIkWZcNTTasPbVitypq6k2KCJ5wIL9M1730lT7Zky+HQ9qXV647F2/SnYs26tDJSrPD82tu51vTbx0wMlz6rrPou+4QkmsAALzEhpwi7TreeBSKxSJNn5hmXkBe5PGrhyskqLF+Ma+0Ri99dsDEiNCZvjpQqOteWqPDRVVur32yJ1+Xv/C5XvhoH+XiHnCsuEoHm/zyIiTIorH96LcOFK591+xcdwzJNQAAXsL1+K3Lh/VQardIc4LxMv2TonTnhemGtVe/yNHBkxUtPAFf8feNhzTjjQ0qq265EqG23q4/fLJfV7zwuT7eRal4Z3ItCR/VN14RoUEmRYOultFMcs1Mi7NHcg0AgBc4cqpSq3aeMKwF4vFbrfnJpQOVHBPmvK612fWb/+w2MSJ0hN3u0PyVe/Tz/9tumI4vSddl9NHYfgluzxwuqtJdSzbpjkUb+cVKJ3ErCaffOqAM6B6tmCatRyVVdcop5L+ts0VyDQCAF3hz7UE1zS+G9ozhh1wX0WHBeux7Qw1rH+3K0xf7CkyKCGerus6mB97O0p8/+8bttce+N1TPf/88/fO+iXrupvOUFB3qds/qPfm64oUv9Dyl4h3munPN+daBxWq16DxKwzsNyTUAACarrK3X2xvcj9+yWALz+K3WXDuqj9sAnnnLd6nOZjcnILRbQVmNfvCXdfrP9uOG9bBgq/5822jdN3mALBaLLBaLbhiTok8eufj0cXQu/znU1tu14JP9uvz5z/XRrjxKWc/C4aJKHS1u7HMPDbZqdKp7xQD8m9t51ww1O2sk1wAAmOzdrKMqbdJvmhAZomsz+pgYkfeyWi2ae80INf29w4H8ci1Ze9C8oNBm+/NOTwR33RlLig7V3+6ZoO+d28vtmbiIEM2dNkLLH7hI49LcE78jp6p0N6XiZ8V113p0arzCQ+i3DjSuv7Bk5/rskVwDAGAih8OhRS7Hb91yfio/4LbivL7xummM8ezv33+0T4XlNSZFhLb47/5CXf/SGh05ZZwIPig5Wu/+eJIyzrBjOrx3rP5x70Q9//3zlBQd5vb6p3sLTpeKf7hXVbWUireFe791kkmRwEyuO9e7j5fSbnGWSK4BADDRVwdOan9+ufM6yGrRDyf0MzEi3zB7ylDDEJ6ymno9u2qviRGhNX/bcEgzF25QmcvZ5BcNStL//fgC9U1s21R8i8Wi60enaPWjkzVrUgul4qsP6IoXPteHO09QKt4Kh8PhtnPN+daBqVt0mPomRjiv6+0O7ThaYmJEvovkGgAAEy38KsdwfeU5PdU7PqKFu9Gge0yYHrp8kGHt75sOa/sRfiD0Jna7Q7/7YI8ee8d9Ivgt56fqjZnjFBse0u73jQ0P0ZxrRug/D7ZcKn7Pm5t1x6KNymXycbNyT1bqRGm18zo8xKrz+saZGBHMlNHX+N8RpeFnh+QaAACT5BZWaPXefMPaHRy/1WbTJ6YpvXuU89rhkOb+eye7lV6ius6mn7y9RS9/bpwIbrFI/3PVUD113TkKCerYj6LDep25VPy7lIo3y3XXemy/RIUF044SqNyGmpFcnxWSawAATLJ4ba6a5oHn9oljUm87hAZb9eurhxvWNh88pfe3HjMpIjQoKKvRzX9ZpxXbjWe3h4ecngh+z3cGdNo0fNdS8SCXWvFa2+lS8cufp1S8Kbd+a0rCA9oo16FmTAw/Kx5Jruvr6/XRRx/p0Ucf1YUXXqju3bsrJCRE8fHxGjNmjB577DEdPNj2qZ6rVq1SZmamevfurfDwcKWmpuq2227T+vXrPRE+AAAeV1Zdp39uOmJYmzWJ47fa6+Ihybp8WLJh7bcf7FaFS28vus6+vDJd+6evtM1tIniY/n7PRF15jvtE8M7QUCq+/IELmy0VP1p8ulR8FqXizfZbT0gnuQ5kI3rHKrRJJcnR4irll1W38gSa45HkevTo0frud7+r5557Tl999ZUKCwtVX1+vkpISbdmyRfPnz9fw4cO1ePHiM77XQw89pCuvvFLLli3T8ePHVVNTo8OHD2vp0qWaNGmSnn32WU/8EQAA8Kh/bT6i8iYJYFJ0mKaO9EzS4e8enzrc8ENhXmmN/vTpARMjClxf7i/QDS+tMZydLEmDe0Trvfsv0Hkupaee0FAq/sLNzZeKf/ZtqfhzAVwq/k1BuWG6fmRokEam0G8dyMKCgzSsd6xhjd3r9vNIcl1aWiqr1aorrrhCf/nLX7Rjxw6dPHlS2dnZ+tOf/qRu3bqpsrJSs2bN0ooVK1p8n+eff14LFiyQJE2dOlUbNmxQQUGBPvvsM02YMEE2m02zZ8/WO++844k/BgAAHmG3O7R4Ta5h7YcTUul3PEtpSVG686L+hrXXvswJ+N3Jrvb2hkOauXBjsxPB//WjC5SS0LaJ4J3BYrHouozTpeJ3TOrfbKn4i9+Wiq8KwFJx113rcWmJHe5/h+/LoO+6wzzyX9H3v/997d69Wx9++KHuvvtujRgxQomJierfv79+/OMf67///a+ioqLkcDg0e/bsZt+jsLBQc+fOlSRdcsklWrZsmcaNG6ekpCRNnjxZq1ev1pAhQyRJjzzyiGpraz3xRwEAoNN9ti9fuScrndchQRbdOj7VxIh8308uGagesY27lLU2u37zn90mRhQ47HaHfrtit37xznbZXCaC3zr+7CeCd4bY8BD9+prh+s+DF+r8tES3148WV+neACwVp98azcmg77rDPJJcP/300xo8eHCLrw8dOlSzZs2SJO3atavZ/uslS5aorKxMkjR//nxZrcZQIyIiNG/ePElSbm5uqzvgAAB4k4Vf5RqurxnZW8kx4eYE4yeiwoL12PeGGtY+3p2nz/cVmBRRYKiqtenHf92iV77INqxbLNIvrxqm/7224xPBO8PQnrH6+70T9PubR6l7TGCXitvtDq3LLjKsTaTfGnKfGP71kWK3X5ihdab9bXfOOec4Pz92zH2q57JlyyRJ/fv317hx45p9j8zMTIWHn/5h5P333/dAlAAAdK79eWX6cn+hYW3WpP4t3I32uHZUH4122Xl54t87VVtvNycgP5dfVq0f/GWtVu5sbiL4GN39nXSvGtBnsVh0bUYfrX5ksu68MHBLxffll6moorHiMzosWCNcem0RmFITI5UYFeq8rqi1aX9+mYkR+R7Tkuu8vDzn53Fx7gMUtmzZIkmaOHFii+8RFhamjIwMSdLmzZs7OUIAADrfIpde6zH9EnQug4Q6hcVi0dxpI9Q0n8suqNCStbmmxeSv9p4o03V/WqNtR0oM691jGiaC9zQpsjOLCQ/Rr67+tlS8f8ul4jMXblSOH5aKu/Zbn98/UcFeUF0A81ksFrfda0rD28e0/5IahpAlJiY6e6cbHD161FkSnp6e3ur7NLy+b98+v/0NIwD/d/BkhY65TNeF/ymprNM7W44a1mZNSjMnGD81MiVe3x/T17D2h4/3q6CspoUn0F5f7i/QjX92nwg+pEeM3rt/UpdMBO8MQ3vG6u/3tFwq/vm+Ak154Qs9u8q/SsVdk2tKwtGUW3LNULN2MSW5XrhwobZt2yZJuvfeexUUZJyOWljYWC7Xo0ePVt8rOfn02ZY1NTUqLy9vVxynTp1q8aO4uLhd7wUAZ8PhcGjusp2a/MxnuujpT/WXL74xOyR40N82HlJVXeMP6b3iwjVlhPfu8Pmq2VcOUUxYsPO6rKZez67aa2JE/mPp+uYngn9ncHf960cT1Sc+wqTIzk5bSsX/+OnpUvGVO3y/VNxud2h9jku/NcPM0ITrULMsdq7bpcuT6127dunBBx+UJPXr108///nP3e6pqGgswWnoqW5JRETjX+LtTa4TExNb/DjTjjkAdIanV+11lgnb7A49tWKPlm1zn0MB31dvs2vJWuMAzx9O6OcVw578TVJ0mB66fJBh7R+bD+vrI8XmBOQH7HaHnlqxW//zrvtE8NvGp+qNGWMVY9JE8M7QUCq+4sGLWiwVv++tzZrh46Xiu46XqqSqznkdGx6sYb3ot0ajkSnxhut9+WUqd/llGlrWpf+i5+fna9q0aSovL1doaKiWLl3abL91098KetMgDADoTAu/ytGfP3PfqZ79z22UYfmhj3fnGcpow4KtuvV8jt/ylBkXpGlgcrTz2uGQ5i7bKTuTb9utqtamH/11s/7SzETwx6cO02+uPcdvenaH9IzR3++ZoD/8oPlS8S++LRV/ZtUeVdb6XsKxzuUIrvHp3dx26xHY4iJCNKB7lPPa4RC/mGyHLvubsKSkRFOmTNE333yjoKAgLV26VBdccEGz90ZHN/5jWFXVeg9i09ebPtcWRUVFLX5kZ2ef+Q0A4Cwt//qY5i3f1exrNfV23b1kk46X0IPtT95wOX7ruow+SmgylRWdKyTIql9fPdywtuVQsd7berSFJ9Cchongq3bmGdbDQ6x6+YdjdNdF3jURvDNYLBZljjpdKn5XC6Xif/r0G13x/BdaueO4T5WK02+NtshITTBcUxredl2SXFdUVOiqq67S1q1bZbFY9Oqrr+qGG25o8f6kpCTn502nijcnPz9f0unJ4e1NrhMSElr8iI+Pb9d7AUBbrfmmUA//fZua/jwWGmz867igrEZ3L9nkkzsjcLfzWIk2uPQ5zmSQmcd9Z3B3XTHcOLvldx/socSxjVqbCP6Peyf6/byAmPAQPf5tqfj4FkvFt2jGwo3KLmhfa6IZ6m12t7+H6LdGcxhqdvY8nlxXV1dr2rRpWrNmjSRpwYIFmjVrVqvP9OnTRzExMZJ0xh3knJwcSdLgwYP97jenAPzPrmOlunfJZtXaGs/dDbZa9Or0sbp3snHWw46jpXrkH9soY/UDi1x2rSemd9PQnvQ5doXHpw5TaJOS5fyyGv1x9QETI/INn+8r0A3NTAQf2vP0RHDXvkx/NqRnjP72bal4cgul4lf+/kuvLxXfeazUMIguITJEQ3rEmBgRvFVzybUvVWiYyaPJdV1dnW644QatXr1akvTb3/5WP/nJT9r07OjRoyVJ69ata/Gempoa53nYY8aM6WC0AOBZh4sqNXPhBrcpu0/fOFKTB3fXz6YM1eXDkg2vfbDjhH7/8b6uDBOd7GR5jd53GVLH8Vtdp1+3KN11UX/D2hv/zfHpoVSe9ta6g7pj0Ua3Hf7Jg7vrn/f53kTwztBQKv7JGUrFL3/uc68tFXftt56Q3k1W+q3RjKE9YxQe0pgmFpTVuP2iDc3zWHJts9l06623asWKFZKkxx9/XI899libn582bZqk0zvXmzZtavaeZcuWqbq6WpKUmZnZwYgBwHOKKmo1Y+EG5buctfuL7w3V9aNTJElBVot+/4MMDe1p3ElYsPqA3qdP1GctXX9ItfWNlQp9EyN02bDWj5lE57r/koHqEdu441hrs+s3Lcw8CGQ2u0P/+59devy9HW4TwX84IVWv+/hE8M5wplLxYyXVuu+tLZr+xgavKxVf65JcUxKOlgQHWTWyT7xhjdLwtvFIcu1wOHTnnXfqX//6lyTppz/9qZ588sl2vcf06dOdpeGPPfaY7Ha74fXq6mrNmTNHkpSWlqarrrqqEyIHgM5XWVuvOxZtVHaBcafsjkn9dc93jKXg0WHBenX6WHVzGXQ1+19fK+vQKY/His5VZ7PrzXXG47dmTExjOm8XiwoL1i++N8yw9smefH22N9+kiLxPZW29fvTWZr36ZY5hvWEi+JOZ/jMRvDOcqVT8y/2FmvL7L/T0Su8oFa+z2bXRtd+aYWZoxSjOuz4rHvlb8sEHH9TixYslSbfddpuefPJJlZeXt/hRX+/+l05SUpLmzp0rSfrkk0+UmZmpTZs2qbCwUF988YUuvfRS7d69W5L03HPPKTSUiasAvE+9za4Hlma5/cb36pG99PjUYc3OiuibGKlXbh9j6BOtrbfrnjc36xhlWT5lxfbjhmqFyNAg3TS2r4kRBa7MUb01pp9xAu685bsMVQWBKr+0Wj/4yzp9uMs4RDYiJEiv+OlE8M7QtFT87ovcS8XrbA699NnpUvEPtptbKr79aIkqam3O66ToMMNRdYCrDIaanRWPJNd//OMfnZ//9a9/VUxMTKsfb731VrPv8/DDD+uBBx6QJC1fvlzjxo1T9+7dNXnyZK1du1ZWq1XPPPOMrr/+ek/8MQCgQxwOh3757g59sse4O3bBgG567vvntdrrNjYtUU9df65hjQnivmehyyCzG8ekKC4isMtqzWKxWPTEtBFqmiNmF1Ro8Zpc02LyBntOlOraP32lr10mgid/OxH8u34+EbwzxISH6JdTh+uDhy7ShPTmS8V/9FdzS8Vdj+CakJ7IL0zQKted6x1HS/hlZBt4fX3PggULtHLlSk2bNk09e/ZUaGioUlJSdMstt2jNmjV69NFHzQ4RAJr1wkf79PdNhw1rw3rF6pXbxygsOOiMz984JsVtgvjOY6V6+O9MEPcFWYdOuf2mf8YFaabEgtPO6ROnH4wzVg784ZP9yi+rNikic322N183/nmtjpUY//wNE8HPTYkzKTLfNLhHjN6+e4IW3JLhdaXirsPM6LfGmfSKizDMqqipt2vPiVITI/INHuu5bs/HzJkzW32/KVOm6P3339fx48dVU1Ojw4cPa+nSpRo/frwnwgeADntz3UEtcDnuJyUhQotnjWvXQKDTE8SNw69W7jyhF5gg7vUWueyITh7cXQO6U4Zptke/O0Qx4cHO6/Kaej2zcq+JEZnjzXUHdefiTW4TwS8e0l3/+tEF6h2AE8E7g8Vi0bTzemv1oxfrnu+kK9gLSsVr6+3alGuc2UG/Ndoio6+xlYbS8DPz+p1rAPA1K3cc16/f32FYS4gM0ZI7zldybHi73uv0BPFRbhPEX2SCuFfLK63Wf74+bljj+C3v0C06TP/v8sGGtX9uPhIwPzTa7A79Zvku/aqZieC3T+in16aPVXRYcAtPo62iw4L1P1cN0wcPXdRsItu0VPwbD5eKbztSrKq6xn7rHrFh6p8U5dGvCf/gWhq+laFmZ0RyDQCdaENOkR7821Y13YyICAnSGzPHKf0sdy2jw4L12oyxSopmgriv+Ou6g6pvkrikd4/SdwZ1NzEiNHX7xH4a5DLMae6ynX7fblFZW6/73tqs1/7rPhH8V1cP17zMEUwE72SDesRo6d3jteCWDEOJbYMv9xfqyt9/ofkeLBV37beemN6Nfmu0ySiXoWZZAfJLyI7gb1AA6CR7T5TprsUbDQM/gqwW/em2DGWkJrTy5JmlJDQ/QfzuJUwQ9zbVdTb9df0hw9rMC9JaHWCHrhUSZNWvrxluWNt6uFjvZvlvNUheabVufmWdPmpmIvhfbh+rOy/sT8LlIQ2l4p880nKp+J8/+0aXPfe5VnigVNwtuabfGm00MiVOTb9dcworVFxZa15APoDkGgA6wbHiKs14Y4NKq407D7+9/lxdOrRHC0+1z5h+ifqtywTxwvIa3bWYCeLe5N/bjulkReMPHzHhwbphdIqJEaE5Fw3qru8ON/63+buVe9x6kP3B7uOnJ4JvP9r8RPArhnfO31Fo3ZlKxY+XVOvHf92i21/foAP5nVMqXl1n0+ZDrv3WSZ3y3vB/kaHBGtIz1rAWKC00Z4vkGgA6qLiyVtPf2KATpcaJu7OnDNH3O/lM4xvGpOi+yQMMa7uOl+r//X2r35e0+gKHw+F2/NbNY/sqih5Wr/T41OEKDW78UaigrEYvrt5vYkSd7/RE8DU6zkRwr9FQKv5iC6Xi/z1QqO/94Qv97oM9qujgL3uyDhUbqqn6xEeobyLD6tB2bqXh9F23iuQaADqgus6muxZvcttlmD6xn3588YAWnuqYn00Z4rbTtGpnnp7/iAniZtuYe0q7jjceVWKxSNMnppkXEFqV2i1S91xkPO7ujf/mKKewwqSIOteb6w7qjkUbVVFrM6xfwkRw01ksFl3zban4vS2Uir/8+Te6/PnP9Z+vz75UfG226/nW9FujfTJch5qxc90qkmsAOEv1NrseeDtLmw4aS+6+d05PzblmhMd+gLFaLfr9ze4TxP/46QG958c9o75g4VfGQVGXD+uh1G6RJkWDtvjxJQPUs8kU/zqbQ08u32ViRB1ns5/+M/zqvR1yLWiZMbGfXmUiuNeIDgvWL74tFb+gmV7o4yXVun/p2ZeKr6PfGh2U4bJzvfVwcZccIeerSK4B4Cw4HA796v2dbsOBzu+fqBduHqUgDw+vimphgvjP/u9rbWGCuCmOnKrUqp0nDGscv+X9IkOD9YurhhrWVu/J16d78k2KqGMqa+t175ub9XozE8HnXDNcT2Sew0RwLzSoR4z+elfnlopX1dqUddil35rkGu00oHu0Ypr8Mq6kqs5vqns8gb9dAeAsLPjkgN7eYJwIPaRHjF6dPlbhIUFdEsPpCeJj3SaI37Nks44yQbzLvbnuoGGXcEiPmGaHFsH7TDuvt8alGSf6P7l8l6FX1RfklVbr+6+s1ce7jb/0iwwN0qu3j9WsSf1Nigxt0dml4psPnlKdrfGe1MRI9aEVAO1ktVp0XjO712geyTUAtNPbGw7phY+N/c2948K1+I7zFRcR0qWxjOmXoN/d0PwE8Y4OwkHbVdbW628bDhvWZk1Ko7fRR1gslm9bORrXsgsrtGhNTssPeZldx05PBN9xtNSw3iP29ETwy5kI7jMaSsVX/rT1UvEfvr6+1VLxtdmFhmt+2Yez5TrUjOS6ZSTXANAOH+3K0y/f3W5Yi4sI0ZI7z1fPuPAWnvKs60en6Ecuw9N2M0G8S72bdVQlVXXO6/jIEF2b0cfEiNBe5/SJ0w/GpRrWFnxyQPll1S084T0+3ZOvm152nwg+rFes3rt/ks7pw0RwXzQw+XSp+B9vzTDMBWjw1YGT+t4fvtBvP9jd7C9TOd8ancV1qBkTw1tGcg0AbbT5YJF+snSLofQ3LNiqN2aO1cDkmJYf7AKzvzvE7czeD3fl6bmP9poUUeBwOBxa5HL81i3np3ZZewA6z6PfHayY8MbewvKaej290rv/G1qyNld3LnafCH7p0GT9876J6hVHGbAvs1gsunpkb33yyGTdO7n5UvFXPs/WZc99ruVfH3OWilfU1OvrI8ZzzUmucbZcd653Hy9VdZ2t+ZsDHMk1ALTBgfwy3bl4k2qa9GBaLdKLt2RoTL9EEyP7NharRS/cPErDesUa1v/06Td6N+uISVEFhq8OnNT+JqWZQVaLbp/Qz8SIcLa6RYfp4SsGG9b+tfmIsrxwSKDN7tAT/96pX7+/020i+MwL0vSX28cwEdyPRIUF6xffa7lU/ERptX6yNOvbUvEybcwtUn2Tb4z0pCj1aGb3G2iLbtFhhvPR6+0O7TxW0soTgYvkGgDO4ERJtWa8sVHFlXWG9d9ce66+O6KnSVG5a2mC+M//b7s2H/S+5MBfuB6/deU5PTk/2If9cEI/DUqONqzN/fcur2qxqKip171vbtJCl4oJ67cTwedOG8FEcD/VllLxK3//pZ5asduwPoFda3RQRl/j0EdKw5vH37wA0IqSqjrNXLjBbfr2Ty8fpFvHp7bwlHn6xEc0O0H83jc3MUHcA3ILK7R6r/HIpjs4fsunhQRZNeeaEYa1bYeL9X9bvKMCpHEiuPH7LjI0SK9OZyJ4IDhTqXi93aF9ecZBZwwzQ0e5loZnMdSsWSTXANCC6jqb7lmySXtOlBnWbzk/VQ9dNsikqM5sTL8Ezb/RdYJ4LRPEPWDx2lw1PQ3n3D5xGp2a0PID8AkXDkrSlBHGGQbzV+5VWXVdC090jYaJ4DuPNT8R/LJhTAQPJE1LxScNbD15nkByjQ4a5TLUbCs7180iuQaAZtjsDj38j61an1NkWL9ieA89mTnC649Yui4jRT9mgrhHlVXX6Z+bjLuZHL/lPx6fOlyhwY0/JhWW1+iPqw+YFk9LE8GH94rV+/dfyETwADYwOUZv3Tlef7p1dLOl4oOSo9U9JsyEyOBPRvSONVTFHS2u8onTFLoayTUAuHA4Tg8KWrH9hGF9TL8EvXhLhs/0Mj7awgTxZz/07unHvuJfm4+ovEklQFJ0mKaO7GViROhMfRMjde930g1rb3yVo+yCls8V9pTFa5qfCH7ZtxPBzToGEN7DYrFo6she+uSRybpv8gBDqfj0C9LMCwx+Iyw4SMN6G4emsnvtzjd+QgSALvTSZ99oydqDhrWBydF6fcZYnzpeqaUJ4i99xgTxjrLbHVq8Jtewdtv4VIUF+873B87sRxcPUK8miWudzaEnl+/qsq9vszs0d9lOzVnWwkTw6WMVxURwNBEVFqzHvjdUHz08Wb+8aphenT5Wt53vffNB4Jsy6Ls+I5JrAGjin5sO65lVxp3dnrHhWnzH+YqPDG3hKe/VOEHcWBL4838xQbwjPtuXr9yTlc7rkCCLbpvAD7D+JjI0WL+4aphh7dO9BVq9J8/jX7uipl73LNmkRS6/xLFapCemjdDcaSMUZKUFAc3rnxSlu7+TriuG95CV7xN0kgz6rs+I5BoAvvXpnnw99s52w1pMeLAW33G++vjw0Up94iP0l+ljDP2jtbbTE8SPnKps5Um0xPUIpGtG9lZyDKW5/uiakb10fprxLPsnl+9WTb2thSc67kTJ6Yngn+xxnwj+2oyxmkGZLwATuE4M//pIsWzMcTEguQYASVmHTunHf91i+EciNNiq16aP1ZCeMSZG1jlGpybo6RtGGtaYIH529ueV6cv9hYa1mRy/5bcsFovmTBuuppt/OYUVbr9g6Sw7j5U0OxG8Z2y4/nnfRF06lIngAMyRmhipxKjGKr6KWpv255e18kTgIbkGEPCyC8p1x6KNqqpr3ImyWKQFPxil8X50fMm1GX10/yXGCeJ7TpTpp0wQbxfXMt0x/RI0MiXelFjQNUb0jtMPXPpWX/xkv/JLO3dS7ie783TTy2t1wuV9R/SO1Xv3T9KI3kwEB2Aei8XitntNabgRyTWAgJZfWq3pb2zQqUrj+bXzpo3Qlef43+TnR64Y4nZ+70e78vQME8TbpKSyTu9sOWpYm8WudUB49LtDFBveODysotam363c02nvv+irHN29ZJMqXSaCXz4sWf+4l4ngALyDW3LNUDMDkmsAAausuk4zF27UkVNVhvWfXDJQt09MMycoD2uYID7cZYL4nz/7Ru9sYYL4mfx90yFDhUOvuHBNGdHTxIjQVRKjQvXwFYMNa+9sOaqsQx0bDNgwEXzuv3e5TQSfNSlNr9zORHAA3sM1uc5i59qA5BpAQKqpt+m+tzZr13FjX+P3x6boke8ObuEp/xAZ2vwE8cf+b7s2HywyKSrvV2+za/Ea4xFtP5zQTyE+cu45Ou6HE/ppcI9ow9rcZTvPuq2itYng8zJHaM41TAQH4F3Oc0mu9+WXqZzZLU78RAAg4NjtDj36z6/11YGThvVLhybrqevOlcXi/z/M9o6P0KvNThDfzATxFny8O09HixurHMKCrbqV82MDSnCQVXOvGWFY23akRP86i6qP4yVVuull94ngUaFBen3GOE330+oZAL4tLiJEA7pHOa8djtNTw3EayTWAgOJwOPSb/+zWv7cdM6yP6huvP96aoeAA2oXMSE3QMzcyQbyt3nCZDn1dRh8lRPne2efomAsGJul75xhbAZ5euVel1XUtPOFux9HTE8FdK2d6xYXrn/ddoEuGJndKrADgCaP6JhiuKQ1vFDg/RQKApFe/zNYbX+UY1tKTovTGzHGKDA28vsbMUX30k0sGGtb2nCjTQ39jgnhTO4+VaEOOsWSe47cC1/9cNUxhTao+Cstr9OIn+9v07Me78vT9V9Yqr7TGsH5On9MTwYf3jm3hSQDwDhmp8YZrhpo1IrkGEDDezTqip1YYp/t2jwnT4jvON5zbGGgevmKwrnQZyvXx7jw9vYoJ4g0WuexaT0zvpqE9SYICVd/ESN37nXTD2sKvcnUgv7zV5xZ+laN73mxuIngP/ePeieoRy0RwAN6vuYnhDge/kJdIrgEEiC/2FWj2P782rMWEBWvxrPPVNzHSpKi8g9Vq0fM3n6cRLjtmL3/+jf5vMxPET5bX6H2XNgKO38KPLh6o3k2Ox6q3O/Tk8l3N/oBZb7Nrzvs79EQzE8HvvLC/Xrl9TEBWzgDwTUN7xig8pDGNLCirMcwkCWQk1wD83tdHinXfW5tV3+Sn2tAgq16ZPoYSzG9Fhgbr1elj1T3GOEH8F+8wQfztDYdUW293XvdNjNBlw3q08gQCQURokH5x1TDD2uf7CrTaZUBZeU297l6ySYvXGifNWy3Sk5kj9KurhzMRHIBPCQ6yamSfeMMapeGnkVwD8Gu5hRWatXCjoQzTYpGev/k8XTAgycTIvE/v+Aj95Xb3CeL3LAncCeJ1NrveXGdMimZMTCMZgiTp6pG9dH7/RMPavOW7VFN/+u+bhongn+4tMNwTFRqk12eO0+1MBAfgo0a59l0z1EwSyTUAP1ZQVqMZCzfoZEWtYf1XU4fr6pG9TYrKuzU3QfxkxekJ4oF4juWK7ccNg6ciQ4N009i+JkYEb2KxWDT3mhFq+ruWgycr9cZ/c50TwXe3NBF8CBPBAfiuDJe+6yx2riWRXAPwU+U19bpj0UYdPGnccb13crruuLC/SVH5hsxRffTApe4TxH/6t62yBdgE8UVrcg3XN45JUVxEiDnBwCsN7x2rW8cbzzt/cfV+3fQyE8EB+C/XnesdR0sMLVSBiuQagN+prbfrR29t1vajJYb16zP66OdThpoUlW/5f5cPdjvL9/QE8T0tPOF/th4udju7c8YFaabEAu/2yBVDDL90qay1qarOOBH8iuFMBAfgP3rFRahHbOOclpp6u/acKG3licBAcg3Ar9jtDv38/77Wl/sLDevfGdxd828cKSu9sm1itVr03PfdJ4i/8nm2/hUgE8QXupyHPnlwdw3oHm1SNPBmCVGheuS7g1t8/a4L++vlHzIRHIB/yeibYLhmqBnJNQA/M3/VHr2bddSwNjIlTn++bbRCgvgrrz0iQ4P12gz3CeL/8852bcr17wnieaXV+s/Xxw1rHL+F1tx6fqqG9owxrAVZLXry2nP0OBPBAfghhpq54ydNAH7j9f/m6JXPsw1r/bpF6o2Z4xQVxo7R2egVF6FXp49VmMsE8Xvf3KzDRf47Qfyv6w4ajm5LT4rSdwZ1NzEieLvgIKueuv5cRYYGSZKiw4L1+oyxun1CP5MjAwDPGMVQMzck1wD8wrJtx/Tk8l2GtaToUC2543wlRYe18BTaYlTfeD0dQBPEq+ts+uv6Q4a1mZPSaCnAGY1OTdDKh76jl24brf/+/BJdzERwAH5sZEqc4bSEnMIKFVfWtvxAACC5BuDz1hwo1CP/2GpYiwoN0sKZ56tftyhzgvIzmaP66EGXCeJ788r00NtZfjdB/N/bjhmOb4sJC9YNo1NMjAi+JLVbpK46t5fiI0PNDgUAPCoyNFhDehpnswR63zXJNQCftvNYie55c7PqbI0JXrDVopdvH6NzU+JMjMz//LSZCeKf7MnX0yv9Z4K4w+FwO37r++P60lYAAEAz3ErDA7zvmuQagM86XFSpmQs3upUmP3vTebqI/thO1zBB/Jw+LhPEv8jWPzcdNimqzrUx95R2Hms8SsRikWZMTDMvIAAAvFiG61Azdq4BwPecLK/R9Dc2qKCsxrD+y6uG6dqMPiZF5f8iQ4P16vSxSnadIP7udm30gwnirsdvXT6sh1K7RZoUDQAA3i3DZed66+FiORz+1S7WHiTXAHxOZW297li8STmFFYb1Oy/sr7u/k25SVIGjV1yE/uIyQbzO5vD5CeJHi6u0aucJwxrHbwEA0LIB3aMV06R1qqSqzu3ns0BCcg3Ap9TZ7Lr/r1u0zaXs6JrzeuuXVw0zJ6gANKpvvJ656TzDWpGPTxBfsjZXTWezDekRo4np3cwLCAAAL2e1WnReM7vXgYrkGoDPcDgc+sU72/Xp3gLD+qSB3fTsTSM5KqmLTTuvtx68bJBhzVcniFfW1utvG4x947Mmpcli4XsKAIDWuA41I7kGAB/w7Id79a/NRwxrw3vF6uUfjlFYcJBJUQW2n142SFed6z5BfL6PTRB/L+uYSqrqnNfxkSH07gMA0AZMDG9Ecg3AJyxek6s/ffqNYa1vYoQW3TFOMeEhJkUFq9Wi524apXP7GI89+8sX2fqHj0wQP338lnGQ2S3npyo8hF/YAABwJqNcJobvPl6q6jqbOcGYzGPJtcPh0O7du7V48WLdf//9GjdunMLCwmSxWGSxWJSbm9vm91q1apUyMzPVu3dvhYeHKzU1VbfddpvWr1/vqfABeJEV249r7r93GtYSo0K1eNb5So4JNykqNIgIDWp2gvgv392uDTneP0H8qwMntS+v3HkdZLXo9gn9TIwIAADfkRQdpr6JEc7rertDO4+VmBiReTyWXB88eFDDhw/XzJkz9dJLL2nTpk2qra1t9/s89NBDuvLKK7Vs2TIdP35cNTU1Onz4sJYuXapJkybp2Wef9UD0ALzFuuyT+unftqrpqQ4RIUF6Y+Y4pXePNi8wGPSMC9erzUwQv+8t758g7nr81pXn9FTv+IgW7gYAAK5G9U0wXAdqaXiXlIX36dNH1113nS666KJ2Pff8889rwYIFkqSpU6dqw4YNKigo0GeffaYJEybIZrNp9uzZeueddzwRNgCT7TlRqruXbFKtze5cC7Ja9NIPR7v198B85/WN17PNTBC/c/FGlVXXtfCUuXILK7R6b75hbdYFaeYEAwCAj3I97zorQIeaeSy57tatm9577z0dP35cR44c0TvvvKNLL720zc8XFhZq7ty5kqRLLrlEy5Yt07hx45SUlKTJkydr9erVGjJkiCTpkUceOatdcQDe62hxlWa8sUFl1cZjnebfMFKXDEk2KSqcyTXn9dZDLhPE9+WV66G/bfXKCeKL1+YaqiLO7ROnMf0SWn4AAAC4ce273srOdeeKiYlRZmamevbseeabm7FkyRKVlZVJkubPny+r1RhqRESE5s2bJ0nKzc3VihUrOhYwAK9xqqJW019fr7zSGsP6z64cohvHpJgUFdrqocsGaeq5vQxrq/fk63cf7DYpouaVVdfpn5uM0+c5fgsAgPYb3itWIUGN/34eLa5Sflm1iRGZw2unhS9btkyS1L9/f40bN67ZezIzMxUefnqY0fvvv99lsQHwnKpam+5cvFHfFFQY1mdekKYfTR5gUlRoD6vVomdvOs9tgvirX+boHxu9Z4L4/20+ovKaxsqIpOgwTR3Zq5UnAABAc8JDgjS8t/Hf/UDcvfba5HrLli2SpIkTJ7Z4T1hYmDIyMiRJmzdv7pK4AHhOvc2uB97O0haXv4yvOrenfnX1cHYUfUiLE8Tf26712SdNiqqR3e7Q4rUHDWu3jU/lvHQAAM4SfddemlwfPXrUWRKenp7e6r0Nr+/bt08OR/v6+U6dOtXiR3Fx8VnFDuDsOBwO/er9Hfp4d55hfXz/RD3//VEKspJY+5qeceF6bUbzE8QPnTR3gvhn+/KVU9hYHRESZNFtE1JNjAgAAN+WQd+1dybXhYWFzs979OjR6r3JyacHG9XU1Ki8vLzVe10lJia2+HGmpB5A5/r9x/v19gZjyfDQnjH6y/SxCg9hN9FXjUyJ13PfN04QP1VZZ/oE8YVf5RqurxnZmzPTAQDoANeTXL4+UuyVw0w9ySuT64qKxt2Ehp7qlkRENJ5F2t7kGoB3+Ov6g/rDJ/sNa33iI7Ro1vmKiwgxKSp0lqtH9tZPLzdOEN+fX64H384y5R/dA/ll+nJ/oWFt5qS0Lo8DAAB/kpoYqcSoUOd1Ra1N+/PLTIyo63llct20vNuTPZZFRUUtfmRnZ3vs6wJotGrnCf3qvR2GtfjIEC2+Y5x6xrGT6C8eumyQ27CwT/cW6Lcrun6CuOuu9Zh+CRqZEt/lcQAA4E8sFovb7nWglYZ7ZXIdHR3t/LyqqqrVe5u+3vS5tkhISGjxIz4+vl3vBaD9NuYW6cG3s9R08zI8xKrXZ4zTwOQY8wJDp7NYLHr2xvM0MsU4SfS1/+bo7xsPdVkcJZV1emfLUcPaLHatAQDoFG7JdYANNfPK5DopKcn5eV5eXit3Svn5+ZJOTw5vb3INwDz78sp056KNqqm3O9esFumPt4zWmH4JJkYGT2mYIN4j1jhB/PH3dnTZBPG/bzqkqjqb87pnbLimjOjZJV8bAAB/55pcZ7Fzbb4+ffooJub0rtWZyrNzcnIkSYMHD+aYHsBHHC+p0ow3Nqi0ut6w/tR15+ry4a0PMYRv6xEbrlenj1V4SNdPEK+32bV4jfH4rdsn9lNIkFf+UwgAgM85zyW53pdfpvKa+uZv9kNe+xPF6NGjJUnr1q1r8Z6amhrnedhjxozpkrjgPwrKavTiJ/s1a+EG3f/XLfr9x/v0wfbj+qagXPU2+5nfAGelpLJOM97YoOMl1Yb1h68YrB+cz1FIgWBkSryeu2mUYa0rJoh/vDtPR4sbW4nCgq26le85AAA6TVxEiAZ0j3JeOxynp4YHimCzA2jJtGnT9Pnnnys7O1ubNm3S2LFj3e5ZtmyZqqtP/4CemZnZ1SHCR207XKzFa3K1/Ovjqm2aRG9v/DQ02KqB3aM1pGeMBveI0ZCe0RqUHKM+8RGyct7yWauus+nuJZu0L8842f+28al64NKBJkUFM0wd2UsH8gfrhY/3Odf255frgbez9PqMcR4519x1kNl1GX2U0GSqKQAA6LhRfRP0TUHj6U9Zh4p1wYCkVp7wH16bXE+fPl1z585VWVmZHnvsMX344YeyWhs32qurqzVnzhxJUlpamq666iqzQoUPqK2364Mdx7VoTW6bej9q6+3adbxUu46XGtajQoM0qEeMhvSI0eCeDf83Wt2jw2hLOAOb3aGf/m2rNuQWGda/O7yH5mWew///AtCDlw3U/vwyLf/6uHPts70FemrFbv3q6uGd+rV2HivR+hzj9x7HbwEA0PkyUuP1f1uOOK8DaaiZR5PrXbt2qbS0MTk5cqTx/8lZWVk6ceKE8zolJUUpKSnO66SkJM2dO1ePPPKIPvnkE2VmZmrOnDlKS0vTrl279Nhjj2n37tNHuDz33HMKDWX3Ae4Kymq0dP0hvbX+oArKajr8fhW1Nm09XOz2l0R8ZMjpHe6mSXePaMVH8n0pnT5eb+6ynVq584RhfVxaghbckuGRXUp4P4vFomdvOk+Hiyq17UiJc/31/+ZoUHJ0p7YJLHLZtZ6Y3k1De8Z22vsDAIDTmpsY7nA4AmIjxeJoeqh0J7v44ov1+eeft+neOXPmaO7cuW7rDz74oF588cVmn7FarZo/f74effTRjoTZrFOnTikxMVHS6fOwExKYXuxLth0u1qI1ufqPa+m3i37dInX7hH6KCgvW3hNl2pdXpn155Sos73giLkk9YsM0uEeMIfEelBytqDCvLRrxiD+u3q9nP9xnWBuUHK1/3XeB4iJDTIoK3iKvtFqZf/xKJ0ob+/CDrRa9ddd4TUjv1uH3P1leo4m/W63aJpPp/3L7GH2XKeEAAHS6eptd58xdpeq6xn93//vzS5SSEGliVM3r7JzP63/CX7BggaZOnaqXXnpJGzZsUFFRkZKTk3XRRRfpoYce0vjx480OEV6iPaXf3xncXTMv6KeLByc320N9srxG+/LKtS+vTHvzyrTvxOn/W1bdvmmHeaU1yiut0Zf7Cw3rfRMjNKRHTGOJeY8YDUiOUlhwULve3xf8Y+Nht8S6Z2y4Ft9xPok1JDVOEL/plTXOf4jr7Q796K3Neu/+SerXLeoM79C6tzccMiTWfRMjdNkwptIDAOAJwUFWjewTb2gF3Hq42CuT687m0Z1rX8bOte/IL6vW0vWH9Nf1h1ot/Y4KDdINY1I0fWKaBia3/0x0h8OhvNIaQ7K979uPpr+ZO1tBVovSukU6h6g1fKR1i1Swjx4V9MnuPN3z5mbZ7I1/zcSGB+tfP7pAg3vEmBgZvNGK7cf1479uMawNTI7WOz++QLHhZ/eLmDqbXRfOX6280sa/Gx6fOkx3XZTeoVgBAEDLnlqxW3/5ovFI5bsu7K/HO3meSmcIuJ1roCUNpd/Lvz6mOlvLvyPq1y1SMyam6caxKWf9A7p0uj+0Z1y4esaFa/Lg7s51u92hw6cqtfdEmfbnlzvLy78pKG81Llc2u0PfFFTom4IKrdje2JscGmTVgORoDekR3aSf2/snl285dEr3L91iSKxDg616bcY4Ems066pze+nhKwbr+Y8aKx0O5JfrgaVZen3G2LP6JdMHO04YEuvI0CDdNLZvp8QLAACa59p3nRUgQ81IruFTGkq/F36Ve8bJg98Z3F2zLkjT5MHdPZqEWq0W9esWpX7dovTdEY3rdTa7cgsrvt3hLte+b5Pu3JMVsrejXqTWZtfu46Xa7TK5PNI5uTz62+PCTife3WPMn1x+IL9cdyzaaNjRt1qkBT/I0Pn9E02MDN7ugUsHan9+uf697Zhz7fN9BXpqxR79+pr2/8Z74Vc5husbRqcoLoJ2BAAAPCkjNd5wveNoiepsdoX4aDVmW5Fcwye0p/T7xjEpmn5BmgZ0b3/pd2cKCbJq0Ld91U1V19l0IN/Yz70vr1xHi6va9f6VtTZtO1ysbS6/ZIiLCHEeETakSXl5V53nm1darRlvbFBxZZ1hfV7mObryHAZIoXUWi0XP3DhSh05WGCaIv/FVjgb1iNYt7ZggvvVwsdv8BY7fAgDA83rFRahHbJizeqym3q49x8t0bkqcyZF5Fsk1vNrWw8Va9FWO/rP9eKsl1mndIjXjgjTdOCZFMR0o/e4K4SFBOqdPnM7pY/zLpay6Tvvzy136ucvbfYRYSVWdNuQWuZ0nnRwTpiE9YzQoOUZDep7e7R7UI0bRnTi5vLS6TjPe2OD2i4IHLx2oH07o12lfB/4tPCRIr04fq2kuE8R/9d4OpXWL0sQBbZsg7rprPXlwd9N/6QYAQKAY1Tdeq3bmSZKSosNUUF4tyb+TawaatYCBZuaprbdrxfbTU7/PVPo9eXB3zeyC0m8zFVXUOgenNfRz7z1RptJ2Ti5vSUpChPOYsMHflpgP6B6t8JD2TS6vqbdpxhsbtC7bmNTfPLavfnfDuaaXqsP37DhaohtfXmNoL4iPDNH7bZggnldarUm/W636Jj0Yi2aN08VDkj0WLwAAaLTmQKGKKms1qm+8+sRHeOXPgp2d85Fct4Dkuuv5Yum3WRwOh/LLagzJdsNOd1WdrcPvb7VIaUlRzrLyId8m3mndopodKmW3O/TA21n6z/bjhvXLhibrldvH+Oy0c5jvg+3H9aOzmCD+/Id7tWD1Aed1elKUPn54st/+Eg4AALQfyXUXIbnuOlmHTmnxmtwzln73T4rS9In9fKL02yx2u0NHi6u0t0lp+d4TZcouqFCtrePHhYUGWZXePcp5XFhD8v3GVzlatCbXcG9GaryW3jVBEaH+d3Y3utaLn+zXcx8Zz0qfPLh7ixPEq+tsmvS71TpZUetcm5c5QtMnpnk6VAAA4EM4igt+oaH0e+GaXLeBXK4mD+6umZPSNHmQ/5Z+dxar1aK+iZHqmxipy4f3cK7X2ew6eLJCe0+UN5aY55Upt7D9k8v3nCjTnhNlrd6X3j1Kb8wYR2KNTvGTbyeIL2vjBPHlXx83JNYxYcG6YXRKl8QKAAACF8k1ulR+abX++m3pd2F566XfN43tq+kT+yk9QEu/O1NIkFUDk2M0MDlGU9XLuV5dZ9M3Bd9OLj9Rrv3fJt1HTrVvcnlTyTFhWnLH+V02nRz+z2Kx6OkbR+pgUaXhl3HNTRB3OBxug8y+P66vojpxcB8AAEBz+GkDXSLr0CktWpOrFW0o/Z4xsZ9uoPS7S4SHBGlE7ziN6G2c3FheU6/9zrLyxt3u/DNMLo8JC9aiWecrJSHSk2EjAIWHBOnV28co809f6XhJyxPEN+ae0s5jjWfCWyzSDMrBAQBAFyC5hsfU1Nu+nfp98Iyl3xcPOT31+zuUfnuF6LBgZaQmKCPV2Hdyqunk8rwy7TtRrr15ZSqpqlNyTJj+eOtoDe8da1LU8HfJseF6dfpY3fTyWufgvnq7Qz/662a99+NJSkuK0qI1xl3ry4f1UGo3ftkDAAA8j+QanS6/tFpvrT+kpWco/Y4OCz499ZvSb5+REBWq8endND698Zxhh8Oh0up6RYcFK4hfjMDDzukTpxduPk/3vdU4Qby4sk53Lt6oP9022nmeZoNZF6R1cYQAACBQkVyj0zSUfv/n6+OGs2VdpX879ZvSb/9gsVgUF8H/jug6V57TS49cMdgwQfybggrd8NIa2Zr83TOkR4yzXBwAAMDTSK7RIc7S769yte1ISav3UvoNoLM0N0G8otZ4xvusSWmyWPi7BgAAdA2Sa5yVxtLvgyosr23xPkq/AXhCSxPEG8RHhujajD5dHxgAAAhYJNdoM4fDoazDxVr01emp32cq/Z5xQZpuGJOiaI7AAeABLU0Ql6Rbzk9VeAjnrAMAgK5D1oMzqqm36T9fH9eiNbn6+gyl35cM6a6Zk/rrooFJlH4D8LjmJogHWy26fUI/kyMDAACBhuQaLcorrdZf1x3U0g2Hzlj6fdPYFE2fmKb+SVFdGCEAnJ4g/tqMsfr5/32tylqbfn31cPWOjzA7LAAAEGBIrmHgcDi05VCxFq9pQ+l39yjNmEjpNwDzTRqYpC9mX0LFDAAAMA0ZESS1r/T70qHJmnFBGqXfALwKfx8BAAAzkVwHuLaWfseEBetGSr8BAAAAoFkk1wGoofR70ZpcfdCG0u+ZF6Tp+tGUfgMAAABAS8iWAkhNvU3Lt50u/d5+tOXSb4tFumRIsmZekKYLKf0GAAAAgDMiuQ4AeaXVemvdQb3dhtLvm8b21fSJ/ZRG6TcAAAAAtBnJtZ86Xfp9SovWHDxj6feAb0u/r6P0GwAAAADOCpmUn2lP6felQ5I1c9Lp0m+LhdJvAAAAADhbJNd+4kRJtf66/qCWrj+kkxWtl35/f9zp0u9+3Sj9BgAAAIDOQHLtwxpKvxd+lauVO060qfT7+tEpiqL0GwAAAAA6FVmWD6qz2fX+1mNaTOk3AAAAAHgFkmsfZHc49LsP9qiwvKbZ12PCg/X9sZR+AwAAAEBXIbn2QWHBQbp1fKoWfLLfsD6ge5RmTuqv6zP6UPoNAAAAAF2IDMxH/XB8ql769IBsDocuG5qsGRdQ+g0AAAAAZiG59lHJseF65qaRGp2aQOk3AAAAAJiM5NqHXZeRYnYIAAAAAABJVrMDAAAAAADA15FcAwAAAADQQSTXAAAAAAB0EMk1AAAAAAAdRHINAAAAAEAHkVwDAAAAANBBJNcAAAAAAHQQyTUAAAAAAB1Ecg0AAAAAQAeRXAMAAAAA0EEk1wAAAAAAdBDJNQAAAAAAHURyDQAAAABAB5FcAwAAAADQQcFmB+Ct7Ha78/Pi4mLzAgEAAAAAdLqmeV7T/O9skVy3oLS01Pl5enq6iZEAAAAAADyptLRU3bp169B7UBYOAAAAAEAHWRwOh8PsILxRfX29Dh8+LEmKjY2V1epdv4coLi527qhnZ2crPj7e3IAQsPhehDfg+xDegO9DeAu+F+ENfOH70G63OyuW+/btq+DgjhV2UxbeguDgYPXv39/sMNokPj5eCQkJZocB8L0Ir8D3IbwB34fwFnwvwht48/dhR0vBm/Ku7VgAAAAAAHwQyTUAAAAAAB1Ecg0AAAAAQAeRXAMAAAAA0EEk1wAAAAAAdBDJNQAAAAAAHcQ51wAAAAAAdBA71wAAAAAAdBDJNQAAAAAAHURyDQAAAABAB5FcAwAAAADQQSTXAAAAAAB0EMk1AAAAAAAdRHINAAAAAEAHkVwDAAAAANBBJNcAAAAAAHQQybWPWrVqlTIzM9W7d2+Fh4crNTVVt912m9avX292aPBj9fX1+uijj/Too4/qwgsvVPfu3RUSEqL4+HiNGTNGjz32mA4ePGh2mAhgBQUFSkpKksVikcVi0cyZM80OCQHmq6++0l133aVBgwYpKipKcXFxGjp0qH7wgx/olVdeMTs8+Lnjx4/rV7/6lcaNG6f4+HiFhIQoMTFREydO1Lx581RQUGB2iPBhDodDu3fv1uLFi3X//fdr3LhxCgsLc/6bm5ub2+b38tdcxuJwOBxmB4H2eeihh7RgwYJmXwsKCtLvfvc7Pfroo10cFQLByJEjtX379lbviYyM1EsvvaQZM2Z0UVRAo9tuu01Lly51Xs+YMUOLFi0yLyAEjJqaGt17771avHhxq/fxYxc8ZdWqVbr55ptVUlLS4j2JiYl699139Z3vfKcLI4O/yM3NVf/+/Vt8PScnR2lpaWd8H3/OZdi59jHPP/+885tx6tSp2rBhgwoKCvTZZ59pwoQJstlsmj17tt555x2TI4U/Ki0tldVq1RVXXKG//OUv2rFjh06ePKns7Gz96U9/Urdu3VRZWalZs2ZpxYoVZoeLALNy5UotXbpU6enpZoeCAGOz2XT99dc7E+tbb71Vq1ev1rFjx1RQUKD169dr7ty5GjBggMmRwl8dOnRIN9xwg0pKShQdHa3//d//1fbt21VQUKAtW7bo//2//6fg4GAVFRXp2muvZQcbHdanTx9dd911uuiii9r1nN/nMg74jIKCAkdMTIxDkuOSSy5x2Gw2w+uVlZWOIUOGOCQ50tLSHDU1NSZFCn81e/Zsx969e1t8fffu3Y6oqCiHJMfw4cO7MDIEuvLyckdaWppDkmPlypUOSQ5JjhkzZpgdGgLA008/7fyee+2118wOBwHol7/8pfN78B//+Eez9/zud79z3vP73/++iyOEPygtLXW89957juPHjzvX5syZ4/y+ysnJafX5QMhl2Ln2IUuWLFFZWZkkaf78+bJajf/zRUREaN68eZJOl22wc4jO9vTTT2vw4MEtvj506FDNmjVLkrRr1y76r9FlHn/8ceXm5urmm2/WlClTzA4HAaS4uFhz586VJN1yyy268847zQ0IAWnr1q2STv8seP311zd7z+233+78fM+ePV0RFvxMTEyMMjMz1bNnz7N6PhByGZJrH7Js2TJJUv/+/TVu3Lhm78nMzFR4eLgk6f333++y2IAG55xzjvPzY8eOmRgJAsXGjRv14osvKi4uTi+88ILZ4SDAvPXWW6qsrJQkzZ492+RoEKjCwsIkSVarVRaLpdl7goKCnJ8nJyd3SVxAU4GQy5Bc+5AtW7ZIkiZOnNjiPWFhYcrIyJAkbd68uUviAprKy8tzfh4XF2diJAgE9fX1uvvuu2Wz2fTUU0+pV69eZoeEANOws9K3b1/nv7/S6T5su91uVlgIMGPGjJEkVVRU6IMPPmj2nn/84x/Oz7/3ve91SVxAU4GQy5Bc+4ijR486yyjONKyn4fV9+/YxlRRdrmEARWJiooYMGWJyNPB3zzzzjLZt26bx48frvvvuMzscBKCNGzdKksaNG6e6ujo988wzGjFihMLDwxUSEqJ+/frpnnvu0f79+02OFP7s/vvvd+5G33777frjH/+oQ4cOqbq6Wt98843mzZvnrKx45JFHNGHCBDPDRQAKlFyG5NpHFBYWOj/v0aNHq/c2/OVaU1Oj8vJyj8YFNLVw4UJt27ZNknTvvfcaStCAznbgwAHNmzdPQUFBevnll916twBPq6qqcv77nJiYqIsuukg/+9nPtGvXLtXX18tut+vQoUN69dVXNXLkSP3rX/8yOWL4q7i4OH355ZcaOXKkTp06pQceeED9+vVTRESEBg4cqDlz5mj06NF6++239eyzz5odLgJQoOQy/CTiIyoqKpyfN/QhtCQiIsL5ua99Q8J37dq1Sw8++KAkqV+/fvr5z39uckTwd/fcc4+qq6v105/+VKNGjTI7HASgpucJL1myROvXr9fFF1+stWvXqqqqSgUFBXr11VcVHx+v6upq/fCHP3QOngI62+DBg/Xuu+/qsssua/b1Y8eOKTs7W/X19V0cGRA4uQzJtY9oWhLR0qAKwCz5+fmaNm2aysvLFRoaqqVLl9JvDY96/fXX9emnnyo1NVVPPPGE2eEgQDXtqa6trdX48eP14YcfasKECQoPD1dSUpLuuusurVixQlarVTU1NZozZ46JEcOfPf300xo0aJC2bNmiP/zhD9q/f7+Kioq0c+dOPfHEEzpx4oR++ctf6nvf+55zCB/QVQIllyG59hHR0dHOz6uqqlq9t+nrTZ8DPKGkpERTpkzRN998o6CgIC1dulQXXHCB2WHBj+Xl5Tl7B1988UVFRUWZHBECleu/sXPmzFFISIjbfRMnTtTUqVMlSatWrVJ1dXWXxIfA8fzzz+vnP/+5QkND9cUXX+jBBx/UwIEDlZCQoOHDh+vXv/61c/Lyxx9/rCeffNLkiBFoAiWXIbn2EUlJSc7Pm05jbk5+fr6k09P2fO0bEr6loqJCV111lbZu3SqLxaJXX31VN9xwg9lhwc/94he/0KlTp3Tttddq2rRpZoeDABYTE+M8AkmSLrroohbvbXitpqZGBw4c8HhsCBx1dXX6zW9+I0m69dZbDUdiNjVlyhRdfPHFkqTXXnvN5wZFwbcFSi5Dcu0j+vTpo5iYGElSdnZ2q/fm5ORIOt17489lFzBXdXW1pk2bpjVr1kiSFixYoFmzZpkcFQJBw9+B7733niwWS7MfDRYvXuxcW7RokUkRw19ZLBYNHTpUkhQcHNzqD4EJCQnOz0tLSz0eGwLHzp07derUKUnS2LFjW7234WzhwsJCFRQUeDw2oEGg5DIk1z5k9OjRkqR169a1eE9NTY3zDLmGMw+BzlZXV6cbbrhBq1evliT99re/1U9+8hOTowKArteQrNTX17eaNJ88edL5eXx8vKfDQgBpWkJ7pt3oQOl7hXcKhFyG5NqHNJQ/Zmdna9OmTc3es2zZMmcvV2ZmZpfFhsBhs9l06623asWKFZKkxx9/XI899pjJUSGQvPbaa8rKymr1o8E111zjXKOEHJ5w3XXXOT//7LPPWryv4bWoqCgNGjTIw1EhkPTu3dv5eUs/HzZoOJc9Ojpa3bp182hcgKuAyGUc8BkFBQWOmJgYhyTHZZdd5rDZbIbXq6qqHMOGDXNIcqSlpTlqampMihT+ym63O2bMmOGQ5JDk+OlPf2p2SECzGr5HZ8yYYXYo8HN1dXWOoUOHOiQ5MjIyHFVVVW73rF692mGxWPiehMcMGjTIIckRHh7u2L59e7P3rFy50vl34/XXX9/FEcJfzZkzx/l9lZOT0+q9gZDLsHPtQ5KSkjR37lxJ0ieffKLMzExt2rRJhYWF+uKLL3TppZdq9+7dkqTnnntOoaGhJkYLf/Tggw9q8eLFkqTbbrtNTz75pMrLy1v84CxNAP4uODhYCxYsUFBQkLKysnTJJZfo448/1smTJ3Xw4EH94Q9/0LRp0+RwOJSYmMjRcfCIhp8Pq6urNXnyZL344ov65ptvVFxcrN27d+vJJ5/UtddeK0kKDQ3Vr371K/OChU/btWuX1q1b5/w4cuSI87WsrKwWX5MCI5exOByMCvQ1Dz74oF588cVmX7NarZo/f74effTRLo4KgaC9/VkLFy7UzJkzPRMM0IqG79UZM2YwyAxdYuHChfrRj36kmpqaZl/v0aOH3nvvPU2YMKGLI0OgePrpp/U///M/stlsLd4TExOjRYsW6frrr+/CyOBPLr74Yn3++edtunfOnDnOZLopf85l2Ln2QQsWLNDKlSs1bdo09ezZU6GhoUpJSdEtt9yiNWvW+Ow3IwAAvmrWrFnKysrSfffdp/T0dIWHhysmJkajR4/W3LlztWvXLhJreNTPfvYzbdu2TQ888IBGjhypmJgYBQUFKT4+XuPGjdMvf/lL7dq1i8QapvPnXIadawAAAAAAOoidawAAAAAAOojkGgAAAACADiK5BgAAAACgg0iuAQAAAADoIJJrAAAAAAA6iOQaAAAAAIAOIrkGAAAAAKCDSK4BAAAAAOggkmsAAAAAADqI5BoAAAAAgA4iuQYAAAAAoINIrgEAAAAA6CCSawAAAAAAOojkGgAAAACADiK5BgAAAACgg0iuAQAAAADoIJJrAAAAAAA6iOQaAAAAAIAOIrkGAAAAAKCDSK4BAAAAAOggkmsAAAAAADqI5BoAAAAAgA4iuQYAAAAAoINIrgEAAAAA6KD/D28LoH1zbpmWAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n@@ -1374,18 +1374,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:01.181989Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:01.181272Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:01.868591Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:01.867633Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:56.904177Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:56.903856Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:57.599123Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:57.598046Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ModuleNotFoundError\",\n \"evalue\": \"No module named 'ipympl'\",\n \"output_type\": \"error\",\n@@ -1415,18 +1415,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:01.872311Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:01.871678Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:02.129103Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:02.128110Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:57.602403Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:57.602043Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:57.841960Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:57.840956Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n@@ -1458,35 +1458,35 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:02.137698Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:02.136985Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.122082Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.120831Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:57.844848Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:57.844541Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.380225Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.379168Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.133138Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.132366Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.261849Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.260769Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.384239Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.383696Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.397940Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.396984Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"<div>\\n\",\n@@ -1622,34 +1622,34 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.271811Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.268597Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.283685Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.279965Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.401143Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.400744Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.405458Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.404592Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from IPython.display import Markdown\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.288625Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.287515Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.304134Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.297852Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.407960Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.407688Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.414434Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.413616Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"\\n\",\n@@ -1721,18 +1721,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.315577Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.314566Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.327684Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.326553Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.417708Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.416723Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.423075Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.422249Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n@@ -1743,15 +1743,15 @@\n \" frameborder=\\\"0\\\"\\n\",\n \" allowfullscreen\\n\",\n \" \\n\",\n \" ></iframe>\\n\",\n \" \"\n ],\n \"text/plain\": [\n- \"<IPython.lib.display.YouTubeVideo at 0x7f37de7b76e0>\"\n+ \"<IPython.lib.display.YouTubeVideo at 0x7f4ebd3c6300>\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1838,18 +1838,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.339834Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.334401Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.353627Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.352390Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.425780Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.425502Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.431945Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.431066Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/javascript\": [\n \"\\n\",\n@@ -1884,18 +1884,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.371244Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.370254Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.379139Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.378073Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.434725Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.434426Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.439822Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.439000Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/x-haskell\": [\n \"main = putStrLn \\\"Hello, world!\\\"\"\n@@ -1925,18 +1925,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.391947Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.386212Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.397350Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.396286Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.442358Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.442048Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.446960Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.446128Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -1966,18 +1966,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.411803Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.406305Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.423724Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.419512Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.450094Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.449439Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.457383Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.456471Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -2024,18 +2024,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.427868Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.426858Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.441497Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.438732Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.460540Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.459961Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.467912Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.467031Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -2070,18 +2070,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:03.445049Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:03.444692Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:03.451373Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:03.450561Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:30:58.471418Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:30:58.470343Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:30:58.477342Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:30:58.476438Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/configuring-kernels.ipynb", "source2": "./usr/share/doc/python-nbsphinx/html/configuring-kernels.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.999609375%", "Differences: {\"'cells'\": \"{5: {'metadata': {'execution': {'iopub.execute_input': '2024-12-15T17:31:05.309178Z', \"", " \"'iopub.status.busy': '2024-12-15T17:31:05.308862Z', 'iopub.status.idle': \"", " \"'2024-12-15T17:31:05.319171Z', 'shell.execute_reply': \"", " \"'2024-12-15T17:31:05.318380Z'}}}}\"}"], "unified_diff": "@@ -63,18 +63,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:15.900606Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:15.899898Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:15.915159Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:15.914275Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:05.309178Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:05.308862Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:05.319171Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:05.318380Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Hello from conf.py!'\"\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/custom-css.ipynb", "source2": "./usr/share/doc/python-nbsphinx/html/custom-css.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9994791666666667%", "Differences: {\"'cells'\": \"{5: {'metadata': {'execution': {'iopub.execute_input': '2024-12-15T17:31:07.905179Z', \"", " \"'iopub.status.busy': '2024-12-15T17:31:07.904807Z', 'iopub.status.idle': \"", " \"'2024-12-15T17:31:07.916200Z', 'shell.execute_reply': \"", " \"'2024-12-15T17:31:07.915286Z'}}}}\"}"], "unified_diff": "@@ -118,18 +118,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:19.347720Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:19.345958Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:19.400193Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:19.395998Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:07.905179Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:07.904807Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:07.916200Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:07.915286Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"42\"\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.html", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.html", "unified_diff": "@@ -117,15 +117,15 @@\n </div>\n <div class=\"nboutput docutils container\">\n <div class=\"prompt highlight-none notranslate\"><div class=\"highlight\"><pre><span></span>[5]:\n </pre></div>\n </div>\n <div class=\"output_area docutils container\">\n <div class=\"highlight\"><pre>\n-<matplotlib.image.AxesImage at 0x7f5a30719160>\n+<matplotlib.image.AxesImage at 0x7efe06e29af0>\n </pre></div></div>\n </div>\n <div class=\"nboutput nblast docutils container\">\n <div class=\"prompt empty docutils container\">\n </div>\n <div class=\"output_area docutils container\">\n <img alt=\"../_images/gallery_cell-metadata_7_1.svg\" src=\"../_images/gallery_cell-metadata_7_1.svg\" />\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -24,15 +24,15 @@\n z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\n [4]:\n zmax = np.max(np.abs(z))\n [5]:\n fig, ax = plt.subplots(figsize=[5, 3.5])\n ax.imshow(z, vmin=-zmax, vmax=zmax)\n [5]:\n-<matplotlib.image.AxesImage at 0x7f5a30719160>\n+<matplotlib.image.AxesImage at 0x7efe06e29af0>\n [../_images/gallery_cell-metadata_7_1.svg]\n *\b**\b**\b**\b**\b**\b* _\bn\bn_\bb\bb_\bs\bs_\bp\bp_\bh\bh_\bi\bi_\bn\bn_\bx\bx *\b**\b**\b**\b**\b**\b*\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bI_\bn_\bs_\bt_\ba_\bl_\bl_\ba_\bt_\bi_\bo_\bn\n * _\bU_\bs_\ba_\bg_\be\n * _\bC_\bo_\bn_\bf_\bi_\bg_\bu_\br_\ba_\bt_\bi_\bo_\bn\n * _\bM_\ba_\br_\bk_\bd_\bo_\bw_\bn_\b _\bC_\be_\bl_\bl_\bs\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.ipynb.gz", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.ipynb.gz", "unified_diff": null, "details": [{"source1": "cell-metadata.ipynb", "source2": "cell-metadata.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9981915020489239%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2024-12-15T17:31:11.184620Z', \"", " \"'iopub.status.busy': '2024-12-15T17:31:11.184228Z', 'iopub.status.idle': \"", " \"'2024-12-15T17:31:12.031025Z', 'shell.execute_reply': \"", " \"'2024-12-15T17:31:12.029963Z'}}}, 3: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2024-12-15T17:31:12.034956Z', 'iopub.status.busy': \"", " \"'2024-12-15T17:31:12.034296Z', 'iopub.status.idle': '2024-12-15T17:31:1 [\u2026]"], "unified_diff": "@@ -35,35 +35,35 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:26.622622Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:26.621789Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:27.632711Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:27.631363Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:11.184620Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:11.184228Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:12.031025Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:12.029963Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:27.639572Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:27.638292Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:27.652123Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:27.647333Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:12.034956Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:12.034296Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:12.039566Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:12.038727Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"plt.rcParams['image.cmap'] = 'coolwarm'\\n\",\n \"plt.rcParams['image.origin'] = 'lower'\"\n ]\n@@ -77,61 +77,61 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:27.656498Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:27.655860Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:27.667939Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:27.666765Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:12.042449Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:12.042134Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:12.047786Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:12.046955Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"x, y = np.meshgrid(np.arange(-3, 3, 0.1), np.arange(-2, 2, 0.1))\\n\",\n \"z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:27.680028Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:27.670984Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:27.684998Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:27.684220Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:12.050491Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:12.050208Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:12.054522Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:12.053683Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"zmax = np.max(np.abs(z))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:27.697096Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:27.696213Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:28.463690Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:28.462637Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:12.057155Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:12.056870Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:12.606650Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:12.605637Z\"\n },\n \"nbsphinx-thumbnail\": {\n \"tooltip\": \"This tooltip message was defined in cell metadata\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n- \"<matplotlib.image.AxesImage at 0x7f5a30719160>\"\n+ \"<matplotlib.image.AxesImage at 0x7efe06e29af0>\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n@@ -173,28 +173,28 @@\n \" <path d=\\\"M 26.925 193.2 \\n\",\n \"L 305.925 193.2 \\n\",\n \"L 305.925 7.2 \\n\",\n \"L 26.925 7.2 \\n\",\n \"z\\n\",\n \"\\\" style=\\\"fill: #ffffff\\\"/>\\n\",\n \" </g>\\n\",\n- \" <g clip-path=\\\"url(#pc5279f0bfb)\\\">\\n\",\n+ \" <g clip-path=\\\"url(#p1aeeaa4656)\\\">\\n\",\n \" <image xlink:href=\\\"data:image/png;base64,\\n\",\n- 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0.8\\\"/>\\n\",\n+ \" <use xlink:href=\\\"#m8fa1bac575\\\" x=\\\"26.925\\\" y=\\\"28.125\\\" style=\\\"stroke: #000000; stroke-width: 0.8\\\"/>\\n\",\n \" </g>\\n\",\n \" </g>\\n\",\n \" <g id=\\\"text_14\\\">\\n\",\n \" <!-- 35 -->\\n\",\n \" <g transform=\\\"translate(7.2 31.924219) scale(0.1 -0.1)\\\">\\n\",\n \" <use xlink:href=\\\"#DejaVuSans-33\\\"/>\\n\",\n \" <use xlink:href=\\\"#DejaVuSans-35\\\" x=\\\"63.623047\\\"/>\\n\",\n@@ -554,15 +554,15 @@\n \" <path d=\\\"M 26.925 7.2 \\n\",\n \"L 305.925 7.2 \\n\",\n \"\\\" style=\\\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\\\"/>\\n\",\n \" </g>\\n\",\n \" </g>\\n\",\n \" </g>\\n\",\n \" <defs>\\n\",\n- \" <clipPath id=\\\"pc5279f0bfb\\\">\\n\",\n+ \" <clipPath id=\\\"p1aeeaa4656\\\">\\n\",\n \" <rect x=\\\"26.925\\\" y=\\\"7.2\\\" width=\\\"279\\\" height=\\\"186\\\"/>\\n\",\n \" </clipPath>\\n\",\n \" </defs>\\n\",\n \"</svg>\\n\"\n ],\n \"text/plain\": [\n \"<Figure size 480x336 with 1 Axes>\"\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.html", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.html", "unified_diff": "@@ -82,15 +82,15 @@\n </div>\n <div class=\"nboutput docutils container\">\n <div class=\"prompt highlight-none notranslate\"><div class=\"highlight\"><pre><span></span>[2]:\n </pre></div>\n </div>\n <div class=\"output_area docutils container\">\n <div class=\"highlight\"><pre>\n-[<matplotlib.lines.Line2D at 0x7f83f652ddc0>]\n+[<matplotlib.lines.Line2D at 0x7f4a6cb19d00>]\n </pre></div></div>\n </div>\n <div class=\"nboutput nblast docutils container\">\n <div class=\"prompt empty docutils container\">\n </div>\n <div class=\"output_area docutils container\">\n <img alt=\"../_images/gallery_cell-tag_4_1.svg\" src=\"../_images/gallery_cell-tag_4_1.svg\" />\n@@ -108,15 +108,15 @@\n </div>\n <div class=\"nboutput docutils container\">\n <div class=\"prompt highlight-none notranslate\"><div class=\"highlight\"><pre><span></span>[3]:\n </pre></div>\n </div>\n <div class=\"output_area docutils container\">\n <div class=\"highlight\"><pre>\n-<matplotlib.collections.PathCollection at 0x7f83f571b3e0>\n+<matplotlib.collections.PathCollection at 0x7f4a6cce4a70>\n </pre></div></div>\n </div>\n <div class=\"nboutput nblast docutils container\">\n <div class=\"prompt empty docutils container\">\n </div>\n <div class=\"output_area docutils container\">\n <img alt=\"../_images/gallery_cell-tag_6_1.svg\" src=\"../_images/gallery_cell-tag_6_1.svg\" />\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -10,23 +10,23 @@\n import matplotlib.pyplot as plt\n The following cell has the nbsphinx-thumbnail tag, which will take precedence\n over the default of the last image in the notebook:\n [2]:\n fig, ax = plt.subplots(figsize=[6, 3])\n ax.plot([4, 9, 7, 20, 6, 33, 13, 23, 16, 62, 8])\n [2]:\n-[<matplotlib.lines.Line2D at 0x7f83f652ddc0>]\n+[<matplotlib.lines.Line2D at 0x7f4a6cb19d00>]\n [../_images/gallery_cell-tag_4_1.svg]\n Although the next cell has an image, it won\u2019t be used as the thumbnail, due to\n the tag on the one above.\n [3]:\n fig, ax = plt.subplots(figsize=[6, 3])\n ax.scatter(range(10), [0, 8, 9, 1, -8, -10, -3, 7, 10, 4])\n [3]:\n-<matplotlib.collections.PathCollection at 0x7f83f571b3e0>\n+<matplotlib.collections.PathCollection at 0x7f4a6cce4a70>\n [../_images/gallery_cell-tag_6_1.svg]\n *\b**\b**\b**\b**\b**\b* _\bn\bn_\bb\bb_\bs\bs_\bp\bp_\bh\bh_\bi\bi_\bn\bn_\bx\bx *\b**\b**\b**\b**\b**\b*\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bI_\bn_\bs_\bt_\ba_\bl_\bl_\ba_\bt_\bi_\bo_\bn\n * _\bU_\bs_\ba_\bg_\be\n * _\bC_\bo_\bn_\bf_\bi_\bg_\bu_\br_\ba_\bt_\bi_\bo_\bn\n * _\bM_\ba_\br_\bk_\bd_\bo_\bw_\bn_\b _\bC_\be_\bl_\bl_\bs\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.ipynb.gz", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.ipynb.gz", "unified_diff": null, "details": [{"source1": "cell-tag.ipynb", "source2": "cell-tag.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9987680190233108%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2024-12-15T17:31:15.125398Z', \"", " \"'iopub.status.busy': '2024-12-15T17:31:15.125061Z', 'iopub.status.idle': \"", " \"'2024-12-15T17:31:15.817441Z', 'shell.execute_reply': \"", " \"'2024-12-15T17:31:15.816230Z'}}}, 4: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2024-12-15T17:31:15.821191Z', 'iopub.status.busy': \"", " \"'2024-12-15T17:31:15.820784Z', 'iopub.status.idle': '2024-12-15T17:31:1 [\u2026]"], "unified_diff": "@@ -27,18 +27,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:31.920020Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:31.919324Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:34.439763Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:34.438478Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:15.125398Z\",\n+ \"iopub.status.busy\": \"2024-12-15T17:31:15.125061Z\",\n+ \"iopub.status.idle\": \"2024-12-15T17:31:15.817441Z\",\n+ \"shell.execute_reply\": \"2024-12-15T17:31:15.816230Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\"\n ]\n },\n@@ -50,28 +50,28 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2026-01-17T23:45:34.449319Z\",\n- \"iopub.status.busy\": \"2026-01-17T23:45:34.448282Z\",\n- \"iopub.status.idle\": \"2026-01-17T23:45:35.051329Z\",\n- \"shell.execute_reply\": \"2026-01-17T23:45:35.050241Z\"\n+ \"iopub.execute_input\": \"2024-12-15T17:31:15.821191Z\",\n+ \"iopub.status.busy\": 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\",\n \"text/plain\": [\n"}]}]}]}]}]}]}