diff --git a/examples/mnist.ipynb b/examples/mnist.ipynb
index bfda893..9719c39 100644
--- a/examples/mnist.ipynb
+++ b/examples/mnist.ipynb
@@ -129,7 +129,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/20 [00:00<?, ?it/s]"
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -140,7 +140,7 @@
     "np.random.seed(0)\n",
     "\n",
     "batch_sz = 64\n",
-    "n_epochs = 20\n",
+    "n_epochs = 10\n",
     "\n",
     "# Flatten images\n",
     "train_images_ = train_images.reshape(-1, 28 * 28)\n",
@@ -166,11 +166,11 @@
     "# Feedforward neural network\n",
     "model = mpp.Sequential(\n",
     "    mpp.Linear(28 * 28, 128),\n",
+    "    mpp.BatchNorm1d(128),\n",
     "    mpp.ReLU(),\n",
     "    mpp.Linear(128, 10),\n",
     ")\n",
     "\n",
-    "\n",
     "def cross_entropy_loss(input_: mpp.Expr, target: mpp.Expr) -> mpp.Expr:\n",
     "    n, _ = input_.shape\n",
     "    input_max = input_.max(dim=1, keepdim=True)\n",
@@ -188,7 +188,7 @@
     "        loss.backward(opt=opt)\n",
     "        opt.step()\n",
     "    test_x = mpp.Constant(test_images_)\n",
-    "    with mpp.no_grad():\n",
+    "    with mpp.eval(), mpp.no_grad():\n",
     "        test_fx = model(test_x)\n",
     "    pred_labels = np.argmax(test_fx.value, axis=1)\n",
     "    accuracy = (pred_labels == test_labels).mean().item()\n",
@@ -203,7 +203,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -231,7 +231,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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0atUqXbx4US+++KJefPFF674uXbooOjq6XPUDAAoj0AIAqrSJEyeqX79+WrlypXbu3KmzZ89KkurXr6/u3bvr8ccfty4gZfQ8QUFB2rFjhxISEnTu3Dn98ssvslgsqlu3rtq1a6fQ0FANHjzYJosGzZgxQx06dNDKlSv1zTff6OLFi6pXr551JrpZs2YlHj98+HDdf//9evvttxUbG6vjx48rLS1Nrq6uqlevnlq2bKnu3btr0KBBatSokeF6SyM0NFRBQUFatGiRdu7cqQsXLsjX11cdOnTQU089pbvvvlt79+41dI7GjRtr165dWrp0qT755BOdPHlSrq6uatGihR566CGNHj1aMTEx1vH5i2iVRYMGDRQbG6uFCxdq9+7dOnv2rLKysgzVDQAomlNaWprF3kUAAIAby3/0zPTp0/Xss8/at5gqbN68eXrllVdUrVo1paSklGuWFgBQOVgUCgAA4P+zWCzatGmTJCkoKIgwCwAOjkALAABuGsnJycrNzS12/yuvvGJd3Oyhhx6qrLIAAOXEPbQAAOCmsXHjRq1Zs0bDhg1Tx44dVb9+feXm5ur48ePasGGDdSXqFi1aWB/zAwBwXARaAABwU0lJSdHixYu1ePHiIvffdtttev/997ncGABMgEALAABuGo888og8PDwUGxurpKQknT9/XleuXJGPj4+CgoI0cOBAjRgxQjVq1LB3qQCAUmCVYwAAAACAKbEoFAAAAADAlAi0AAAAAABTItACAAAAAEyJQItSy8rKUlJSkrKysuxdimnRQ2Pon3H00Dh6aBw9NIb+GUcPjaOHxtA/2yHQokyuXbtm7xJMjx4aQ/+Mo4fG0UPj6KEx9M84emgcPTSG/tkGgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEqmCrRbtmzRkCFDFBAQID8/P7Vp00Zjx45VSkpKgXHp6el67rnnFBQUpHr16ik4OFizZs1SRkaGnSoHAAAAANhaNXsXUBoWi0WTJ0/W6tWrFRAQoGHDhsnT01Pnzp3T7t27dfr0aTVq1EiSlJmZqZCQEB05ckS9evVSaGioEhIStGTJEu3evVsxMTFyc3Oz82cEAAAAADDKFIH29ddf1+rVqzVu3DjNnTtXLi4uBfbn5uZa/7x48WIdOXJE4eHhioiIsG6PiIhQVFSUli9frilTplRW6QAAAACACuLwlxxfuXJFc+fOVdOmTTVnzpxCYVaSqlW7nsstFovWrVsnT09PTZs2rcCYadOmydPTU2vXrq2UugEAAAAAFcvhZ2hjY2OVlpam4cOH69q1a4qJiVFiYqK8vb3Vo0cPBQYGWscmJibq3Llz6t27tzw8PAq8joeHhzp06KBt27YpJSXFeokyAAAAAMCcHD7QHjp0SJLk4uKiLl266MSJE9Z9zs7OGj9+vF5++WVJ1wOtpAIh9/cCAwO1bds2JSYm3jDQZmVl2aD6qiU7O7vA7yg7emgM/TOOHhpHD42jh8bQP+PooXH00Bj6V7yyrnfk8IH2/PnzkqRly5apbdu2io2NVYsWLZSQkKDw8HAtXbpUAQEBGjt2rNLT0yVJ3t7eRb6Wl5eXJFnHleTs2bO6du2ajT6LqiU1NdXeJZgePTSG/hlHD42jh8bRQ2Pon3H00Dgz9/CeXe52rsBd0m92reCrrpftev4/cnFxKXZysjgOH2jz8vIkSa6urlq/fr3q168vSercubNWr16trl27aunSpRo7dqxNz9ugQQObvl5VkJ2drdTUVPn5+cnV1dXe5ZgSPTSG/hlHD42jh8bRQ2Pon3H00Liq0cML9i7A7ho3bmzvEgxz+ECbP6varl07a5jN16pVKzVt2lRJSUlKS0uzjr106VKRr5U/M5s/riQ82qd4rq6u9McgemgM/TOOHhpHD42jh8bQP+PooXH00NyqwtfO4Vc5bt68uaTiLyPO356VlaVmzZpJkpKSkoocm789fxwAAAAAwLwcfoa2W7dukqTjx48X2peTk6OkpCR5eHjI19dXfn5+ql+/vvbt26fMzMwCKx1nZmZq3759atKkCSscAwAAAEAV4PAztAEBAerVq5eSkpIKPUN20aJFunTpkkJCQlStWjU5OTlp5MiRysjI0Lx58wqMnTdvnjIyMjRq1KjKLB8AAAAAUEEcfoZWkhYsWKC+fftq4sSJio6OVvPmzZWQkKCdO3eqcePGeumll6xjJ02apJiYGEVFRSkhIUFt27bV4cOHFRsbq/bt2yssLMyOnwkAAAAAwFYcfoZWuj5L++WXX+qRRx7RoUOHtHLlSiUlJemvf/2rYmNj5efnZx3r4eGh6OhohYWF6fjx41q6dKmOHz+uCRMm6JNPPlHNmjXt+JkAAAAAAGzFFDO0ktSoUSMtX768VGO9vb0VGRmpyMjICq4KAAAAAGAvppihBQAAAADgjwi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlEwRaIODg+Xj41Pkr5CQkELjr169qrlz56p9+/by8/NTy5YtNWnSJP3yyy92qB4AAAAAUBGq2buA0vLy8lJYWFih7f7+/gU+zsvL0yOPPKJt27bpnnvu0aBBg5SYmKi1a9dqx44d+uKLL+Tr61tZZQMAAAAAKohpAq23t7eeffbZG4579913tW3bNoWGhuqf//ynnJycJElvvfWWpkyZopdffllRUVEVXC0AAAAAoKKZ4pLjsli7dq0k6fnnn7eGWUkaPXq0mjZtqg8++EBXrlyxV3kAAAAAABsxTaDNzs7W+vXrtWDBAr3xxhs6cOBAoTFZWVk6cOCAmjdvXuhSZCcnJ/Xs2VOZmZn65ptvKqtsAAAAAEAFMc0lx6mpqXrqqacKbGvfvr1WrVqlgIAASdLJkyeVl5enwMDAIl8jf3tiYqI6d+5c4vmysrJsUHXVkp2dXeB3lB09NIb+GUcPjaOHxtFDY+ifcfTQOHpYNThi5nFzcyvTeFME2uHDh6tTp05q1aqVPDw8dOLECS1btkwbN27UoEGDFB8fr1q1aik9PV3S9ftti+Ll5SVJ1nElOXv2rK5du2a7T6IKSU1NtXcJpkcPjaF/xtFD4+ihcfTQGPpnHD00ztw9dLd3AXZ3+vRpe5dQgIuLS7GTk8UxRaCdMWNGgY/btGmjlStXSpI2btyoNWvWaMKECTY9Z4MGDWz6elVBdna2UlNT5efnJ1dXV3uXY0r00Bj6Zxw9NI4eGkcPjaF/xtFD46pGDy/YuwC7a9y4sb1LMMwUgbY4o0eP1saNG7Vv3z5NmDDBOgN76dKlIsfnz8zmjytJWae6byaurq70xyB6aAz9M44eGkcPjaOHxtA/4+ihcfTQ3KrC1840i0IVpU6dOpKky5cvS5KaNm0qZ2dnJSUlFTk+f3uzZs0qp0AAAAAAQIUxdaDNX+k4f0XjmjVr6q677tIPP/yg5OTkAmMtFou+/PJLeXh46M4776z0WgEAAAAAtuXwgfb48ePWGdg/bo+IiJAkhYaGWrePGjVKkjR79mxZLBbr9rfffls//vijHnjgAdWsWbNiiwYAAAAAVDiHv4f2o48+0vLly9W5c2c1btxY7u7uOnHihLZu3aqcnBxNmTJFXbp0sY5/5JFHtGnTJn344Yc6deqUunTpoqSkJG3ZskVNmjTRzJkz7fjZAAAAAABsxeEDbbdu3XT8+HElJCRoz549unz5surUqaP77rtP48aNU69evQqMd3Z21rvvvqtFixZp48aNWr58uWrXrq2RI0dq5syZ8vX1tdNnAgAAAACwJYcPtF27dlXXrl3LdEyNGjU0Y8aMQo/7AQAAAABUHQ5/Dy0AAAAAAEUh0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwpWr2LgAAAABA2fi8fcbeJUhyl3TBbmdPG93QbueG42CGFgAAAABgSgRaAAAAAIApEWgBAAAAAKZEoAUAAAAAmBKBFgAAAABgSgRaAAAAAIAp8dgeAAAAVDr7P3aGR84AVQEztAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJSq2bsAAObi8/YZO1fgLumCXStIG93QrucHAADAdczQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAl0wbaqKgo+fj4yMfHR1999VWh/enp6XruuecUFBSkevXqKTg4WLNmzVJGRoYdqgUAAAAA2JopA+2xY8cUGRkpDw+PIvdnZmYqJCREy5cvV4sWLTR+/Hg1b95cS5Ys0aBBg5SVlVXJFQMAAAAAbM10gTYnJ0dhYWEKDg5WSEhIkWMWL16sI0eOKDw8XB9//LEiIiL08ccfKzw8XAcPHtTy5csruWoAAAAAgK2ZLtDOnz9f3333nZYuXSoXF5dC+y0Wi9atWydPT09NmzatwL5p06bJ09NTa9euraxyAQAAAAAVpJq9CyiLQ4cOacGCBXruuefUsmXLIsckJibq3Llz6t27d6FLkj08PNShQwdt27ZNKSkpatSoUbHn4rLkwrKzswv8jrKjh1WDmf994D1oHD00jh4aQ/+qBjN/L3EU9NA4R+yhm5tbmcabJtBevXrVeqnxpEmTih2XmJgoSQoMDCxyf2BgoLZt26bExMQSA+3Zs2d17do1Y0VXUampqfYuwfTM3UN3exdgd6dPn7Z3CYaZ+z3oGOihcfTQGPP37+b+fmL8e8nN3T+JHtqCo/2fxsXFpdgcVxzTBNpXX31ViYmJ2r59e5GXGudLT0+XJHl7exe538vLq8C44jRo0KCclVZd2dnZSk1NlZ+fn1xdXe1djilVjR5esHcBdte4cWN7l1BuVeM9aF/00Dh6aEzV6d/N/f3E+PeSm7t/Ej20BTP/nyafKQLt/v37tWTJEs2YMUOtWrWqlHOWdar7ZuLq6kp/DKKH5lYVvna8B42jh8bRQ2Pon7nxtTOOHhpXFXro8ItC5ebmKiwsTK1bt9bkyZNvOD5/BvbSpUtF7s+fmc0fBwAAAAAwJ4efoc3IyLDeF1u3bt0ix9x3332SpHfeece6WFRSUlKRY/O3N2vWzNalAgAAAAAqkcMH2ho1amjkyJFF7ouPj1diYqL69+8vX19f+fv7q1mzZqpfv7727dunzMzMAisdZ2Zmat++fWrSpEmJC0IBAAAAAByfwwfamjVrasmSJUXuCwsLU2JioqZMmaJ77rnHun3kyJH6xz/+oXnz5ikiIsK6fd68ecrIyNCUKVMqumwAAAAAQAVz+EBbHpMmTVJMTIyioqKUkJCgtm3b6vDhw4qNjVX79u0VFhZm7xIBAAAAAAY5/KJQ5eHh4aHo6GiFhYXp+PHjWrp0qY4fP64JEybok08+Uc2aNe1dIgAAAADAIFPP0K5YsUIrVqwocp+3t7ciIyMVGRlZyVUBAAAAACpDlZyhBQAAAABUfQRaAAAAAIApEWgBAAAAAKZk6ntoAcCMfN4+Y+cK3CVdsNvZ00Y3tNu5AQBA1cIMLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEyJQAsAAAAAMCUCLQAAAADAlAi0AAAAAABTItACAAAAAEzJUKBt27atxowZU6qxY8eOVbt27YycDgAAAAAAq2pGDk5OTlaDBg1KNTY1NVXJyclGTgcAgCTJ5+0zdq7AXdIFu509bXRDQ8fbv3+S2XsIAHAMlXbJcW5urpyducIZAAAAAGAblZIwc3JylJiYqNq1a1fG6QAAAAAAN4EyXXK8e/du7dq1q8C2lJQUzZ07t9hjrly5oj179ujChQu67777ylclAAAAAAB/UKZAGxcXp7lz58rJycm67cyZMyUGWkmyWCxyd3fX1KlTy1clAAAAAAB/UKZAGxwcrIcfftj68YYNG1S3bl317t27yPFOTk5yd3dXQECABg8erIYNWYABAAAAAGAbZQq0ISEhCgkJsX68YcMGBQYGavny5TYvDAAAAACAkhh6bM/hw4fl5uZmq1oAAAAAACg1Q4HW39/fVnUAAAAAAFAmhgLt7/322286efKkMjIyZLFYih3XpUsXW50SAAAAAHATMxxoDx06pJkzZ2rPnj0lBlnp+iJRFy5cMHpKAAAAAACMBdpDhw4pJCREV65ckcViUY0aNeTr6ytnZ2db1QcAAAAAQJEMBdrIyEhdvnxZHTp00Ny5c9W2bVtb1QUAAAAAQIkMBdp9+/bJzc1NGzZsUO3atW1VEwAAAAAAN2To2uDs7Gw1b96cMAsAAAAAqHSGAm1AQIAuX75sq1oAAAAAACg1Q4H2kUceUVJSkhISEmxVDwAAAAAApWIo0IaFhalnz5569NFHtW/fPlvVBAAAAADADRlaFOrpp5+Wr6+v4uLi1L9/f7Vu3Vq33Xab3N3dixzv5OSkpUuXGjklAAAAAACSDAbad999V05OTrJYLJKkb7/9Vt9++22x4wm0AAAAAABbMRRop0+fbqs6AAAAAAAoE0OBdsaMGbaqAwAAAACAMjG0KBQAAAAAAPZCoAUAAAAAmJKhS453795d5mO6dOli5JQAAAAAAEgyGGgHDhwoJyenUo93cnLShQsXjJwSAAAAAABJBgNto0aNig20ly9ftoZXV1dX+fn5GTkVAAAAAAAFGAq0R44cKXF/Wlqa3nzzTS1atEiPPvqonnnmGSOnAwAAAADAylCgvREfHx8988wzCgwM1Lhx49SqVSsNGDCgIk8JAAAAALhJVMoqx0OHDlW9evW0bNmyyjgdAAAAAOAmUGmP7WnQoMENL1EGAAAAAKC0KiXQ5uXlKSkpSdeuXauM0wEAAAAAbgIVHmhzcnL03HPP6dKlS2rVqlVFnw4AAAAAcJMwtCjUU089Vew+i8WiX375RQkJCfrll1/k5ORU4vjiZGVlafbs2frmm2908uRJXbx4Ud7e3goICNDIkSP1l7/8RdWrVy9wTHp6uubMmaPNmzfr559/lp+fn4YMGaLp06fL09OzzDUAAAAAAByPoUD77rvvysnJSRaLpcRxHh4eev755zVkyJAynyMzM1NvvfWW2rdvr759+8rX11dpaWnaunWrJkyYoI8//lgffvihnJ2dreNDQkJ05MgR9erVS6GhoUpISNCSJUu0e/duxcTEyM3NrTyfLgAAAADAgRgKtNOnTy92n5OTk9zd3dWsWTN179693DOjtWvXVnJyslxdXQtsz83N1ZAhQxQbG6utW7eqX79+kqTFixfryJEjCg8PV0REhHV8RESEoqKitHz5ck2ZMqVctQAAAAAAHIehQDtjxgxb1VEsZ2fnQmFWkqpVq6aBAwdq165dSkpKknT9Mud169bJ09NT06ZNKzB+2rRpevPNN7V27VoCLQAAAABUAZX22B5by8vL07Zt2yTJuthUYmKizp07pw4dOsjDw6PAeA8PD3Xo0EE//vijUlJSKr1eAAAAAIBtGZqh/aPc3FwlJyfrt99+U61ateTv769q1WxziuzsbC1YsEAWi0UXL17Ujh07dPz4cQ0fPlz33nuvpOuBVpICAwOLfI3AwEBt27ZNiYmJatSoUYnny8rKskndVUl2dnaB31F29LBq4N8HY+ifcfTQODP3kO8lVYOZ34OOgh4a54g9LOt6RzZJmwcPHtS8efO0Y8eOAk1xc3NTz5499cwzz+jOO+80dI7s7GzNnTvX+rGTk5OefvppvfDCC9Zt6enpkiRvb+8iX8PLy6vAuJKcPXuW5+YWIzU11d4lmJ65e+hu7wLs7vTp0wZf4ebuofH+SfSQ96BRtnkf2pe5v5dIN/v7kL/HxtFD4xzt30IXF5diJyeLYzjQrlmzRs8884yuXbtWaLXjK1euKCYmRv/5z3+0YMECPfroo+U+j6enp9LS0pSXl6dz587p888/1+zZs/XVV1/p/ffft4ZVW2nQoIFNX68qyM7OVmpqqvz8/Iq8rxk3VjV6eMHeBdhd48aNDb7Czd1D4/2T6CHvQaNs8z60j6rxvUS62d+H/D02jh4aZ+Z/C/MZCrSHDx/W1KlTde3aNXXq1ElPP/20WrVqpVtvvVU//fSTjh07piVLlmjPnj2aMmWK2rZtq7Zt2xoq2NnZWQ0bNtTYsWNVp04dPfbYY1qwYIFefPFFa6i9dOlSkcfmz8yWJvzyaJ/iubq60h+D6KG58bUzhv4ZRw+Nqwo95HuJufG1M44eGlcVemhoUailS5fq2rVrmjBhgmJiYtS/f381adJENWrUUJMmTdS/f3/FxMTo6aef1rVr17Rs2TJb1S1J6tmzpyRp165dkqRmzZpJknXV4z/K354/DgAAAABgXoYCbXx8vLy9vfX888+XOG7WrFny8vLS7t27jZyukJ9++kmSVL16dUnXg2r9+vW1b98+ZWZmFhibmZmpffv2qUmTJjdcEAoAAAAA4PgMBdpffvlFzZo1swbK4lSvXl233Xabzp8/X+ZzfPfdd7p8+XKh7ZcvX9bf//53SdJ9990n6fpCUSNHjlRGRobmzZtXYPy8efOUkZGhUaNGlbkGAAAAAIDjMXQPraenZ6lX2EtNTS30bNjS2LRpk5YvX66OHTvK399ftWrV0tmzZ/XFF1/o119/VadOnTR+/Hjr+EmTJikmJkZRUVFKSEhQ27ZtdfjwYcXGxqp9+/YKCwsrcw0AAAAAAMdjKNC2adNGO3fuVExMjAYMGFDsuOjoaJ05c8b6vNiyuP/++/XTTz9p//792r9/vzIzM+Xl5aXWrVtr2LBhGjFiRIFn3Xp4eCg6Olpz5szRli1bFBcXJz8/P02YMEHTp09XzZo1y/W5AgAAAAAci6FAO2LECO3YsUOPP/64ZsyYoTFjxsjd/X/Pc7p8+bJWrVqluXPnWi8HLqs777yzzM+w9fb2VmRkpCIjI8t8PgAAAACAORgKtKGhodqyZYs2b96s559/Xq+++qr8/f1Vr149/fzzz0pOTlZWVpYsFosGDx6sYcOG2apuAAAAAMBNztCiUJL01ltvafr06fL09NSVK1f0/fffKy4uTt9//72uXLkiT09PzZgxQ6tWrbJFvQAAAAAASDI4QytJLi4umjFjhiZOnKg9e/bohx9+UEZGhjw9PdWiRQt17NixwGXIAAAAAADYguFAm8/d3V29e/dW7969bfWSAAAAAAAUq8yXHIeEhOiWW27RggULSjV+wYIFuuWWW/TnP/+5zMUBAAAAAFCcMgXa+Ph4xcfHq127dpo6dWqpjpk6daratWunHTt2aP/+/eUqEgAAAACAPypToP3oo4/k5OSkyZMnl+kkU6dOlcVi0QcffFCm4wAAAAAAKE6ZAu2+ffvk5uam++67r0wn6dOnj9zc3LRv374yHQcAAAAAQHHKFGiTk5Pl7+8vNze3Mp2kRo0aatKkiU6dOlWm4wAAAAAAKE6ZAm3+c2XLI/85tQAAAAAA2EKZHtvj4+OjCxculOtEFy5ckLe3d7mOBQAAcCQ+b5+xcwXuksr3fzJbSRvd0K7nBwCpjDO0+ZcN//LLL2U6yc8//6xTp06pSZMmZToOAAAAAIDilCnQduvWTZK0atWqMp1k1apVslgs6t69e5mOAwAAAACgOGUKtKNGjZKLi4uioqK0a9euUh0TFxenqKgoVatWTY8++mi5igQAAAAA4I/KFGibNm2qJ598UlevXtWwYcP06quvFntP7YULF/TKK68oNDRUOTk5evzxx9W0aVNb1AwAAAAAQNkWhZKkF198USdPnlR0dLTmz5+vhQsXqmXLlmratKk8PDyUmZmpH3/8Ud99953y8vJksVg0YMAAvfTSSxVRPwAAAADgJlXmQOvs7Kx33nlHS5Ys0aJFi3Tx4kUdPXpUR48elZOTkywWi3Vs7dq1FR4erokTJ9q0aAAAAAAAyhxo8z399NMaO3astm7dqj179ujs2bP67bffVKtWLTVo0ECdOnVSnz595OHhYct6AQAAAACQZCDQSpK7u7sGDx6swYMH26oeAAAAAABKpUyLQgEAAAAA4CgItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAU3L4QHv27FktX75cf/7znxUUFKS6deuqRYsWGjlypA4cOFDkMenp6XruuecUFBSkevXqKTg4WLNmzVJGRkYlVw8AAAAAqCjV7F3AjbzxxhuKiopSQECAevbsKV9fXyUmJio6OlrR0dF68803NXToUOv4zMxMhYSE6MiRI+rVq5dCQ0OVkJCgJUuWaPfu3YqJiZGbm5sdPyMAAAAAgC04fKBt3769Pv30U3Xt2rXA9vj4eA0ePFhTpkxRSEiIatSoIUlavHixjhw5ovDwcEVERFjHR0REKCoqSsuXL9eUKVMq81MAAAAAAFQAh7/keNCgQYXCrCR17txZ3bp1U1pamo4dOyZJslgsWrdunTw9PTVt2rQC46dNmyZPT0+tXbu2UuoGAAAAAFQshw+0JalevbokycXFRZKUmJioc+fOqUOHDvLw8Cgw1sPDQx06dNCPP/6olJSUSq8VAAAAAGBbDn/JcXFOnz6t7du369Zbb1Xr1q0lXQ+0khQYGFjkMYGBgdq2bZsSExPVqFGjEl8/KyvLtgVXAdnZ2QV+R9nRw6qBfx+MoX/G0UPj6KFx9NAY+mccPTTOEXtY1vWOTBloc3Jy9MQTT+jq1auKiIiwztCmp6dLkry9vYs8zsvLq8C4kpw9e1bXrl2zUcVVS2pqqr1LMD1z99Dd3gXY3enTpw2+ws3dQ+P9k+gh70Gj6KFx9NAY+mccPTTONt+TbcfFxaXYycnimC7Q5uXlafz48YqPj9eoUaP00EMPVch5GjRoUCGva2bZ2dlKTU2Vn5+fXF1d7V2OKVWNHl6wdwF217hxY4OvcHP30Hj/JHrIe9AoemgcPTSG/hlHD42zzfdk+zJVoM3Ly9NTTz2lDz74QA8++KAWLVpUYH/+DOylS5eKPD5/ZjZ/XEl4tE/xXF1d6Y9B9NDc+NoZQ/+Mo4fG0UPj6KEx9M84emhcVeihaQJt/szse++9p9DQUK1YsULOzgXXtGrWrJkkKSkpqcjXyN+ePw4AAAAAYF6mWOX492F26NChWrlypfW+2d9r1qyZ6tevr3379ikzM7PAvszMTO3bt09NmjS54YJQAAAAAADH5/CBNv8y4/fee09DhgzRG2+8UWSYlSQnJyeNHDlSGRkZmjdvXoF98+bNU0ZGhkaNGlUZZQMAAAAAKpjDX3I8d+5cbdiwQZ6enrrtttsKBVVJCgkJUZs2bSRJkyZNUkxMjKKiopSQkKC2bdvq8OHDio2NVfv27RUWFlbZnwIAAAAAoAI4fKBNTk6WJGVkZGj+/PlFjvH397cGWg8PD0VHR2vOnDnasmWL4uLi5OfnpwkTJmj69OmqWbNmpdUOAAAAAKg4Dh9oV6xYoRUrVpTpGG9vb0VGRioyMrKCqgIAAAAA2JvD30MLAAAAAEBRCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFOqZu8CUDY+b5+xcwXuki7Y7expoxva7dwAAAAAHAsztAAAAAAAUyLQAgAAAABMiUALAAAAADAlAi0AAAAAwJQItAAAAAAAUyLQAgAAAABMicf24KZi/8ceSTz6CAAAALANU8zQbty4UeHh4erRo4fq1asnHx8frV+/vtjx6enpeu655xQUFKR69eopODhYs2bNUkZGRiVWDQAAAACoSKaYoX355Zd1+vRp1alTR35+fjp9+nSxYzMzMxUSEqIjR46oV69eCg0NVUJCgpYsWaLdu3crJiZGbm5ulVg9AAAAAKAimGKGdsmSJUpISFBiYqLGjBlT4tjFixfryJEjCg8P18cff6yIiAh9/PHHCg8P18GDB7V8+fJKqhoAAAAAUJFMEWh79Oghf3//G46zWCxat26dPD09NW3atAL7pk2bJk9PT61du7aiygQAAAAAVCJTXHJcWomJiTp37px69+4tDw+PAvs8PDzUoUMHbdu2TSkpKWrUqFGJr5WVlVWRpaKc+LoYRw+No4fG0D/j6KFx9NA4emgM/TOOHhrniD0s6+2hVS7QSlJgYGCR+wMDA7Vt2zYlJibeMNCePXtW165ds3mNxrnbuwC7Kun+6dK5ufsn0UNboIfGGO+fRA95DxpFD42jh8bQP+PooXG2+Z5sOy4uLsVmueJUqUCbnp4uSfL29i5yv5eXV4FxJWnQoIHtCrMp+z3uxRE0btzY4Cvc3P2T6KEt0ENjjPdPooe8B42ih8bRQ2Pon3H00DjbfE+2ryoVaG2JlZAdE18X4+ihcfTQGPpnHD00jh4aRw+NoX/G0UPjqkIPTbEoVGnlz8BeunSpyP35M7P54wAAAAAA5lWlAm2zZs0kSUlJSUXuz9+ePw4AAAAAYF5VLtDWr19f+/btU2ZmZoF9mZmZ2rdvn5o0aXLDBaEAAAAAAI6vSgVaJycnjRw5UhkZGZo3b16BffPmzVNGRoZGjRplp+oAAAAAALZkikWh1q5dqz179kiSjh07Jklat26ddu3aJUnq1KmTHn30UUnSpEmTFBMTo6ioKCUkJKht27Y6fPiwYmNj1b59e4WFhdnnkwAAAAAA2JQpAu2ePXu0YcOGAtv27t2rvXv3Wj/OD7QeHh6Kjo7WnDlztGXLFsXFxcnPz08TJkzQ9OnTVbNmzUqtHQAAAABQMUwRaFesWKEVK1aUery3t7ciIyMVGRlZgVUBAAAAAOypSt1DCwAAAAC4eRBoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKVXZQHvw4EE98MAD8vf3V4MGDdSnTx9t2rTJ3mUBAAAAAGykmr0LqAg7d+7UsGHD5ObmpqFDh8rT01ObN2/W6NGjlZKSoqefftreJQIAAAAADKpygTY3N1eTJk2Ss7OzoqOj1aZNG0nS3/72N/Xu3VsvvfSSBg8eLH9/fztXCgAAAAAwosoF2p07d+rkyZMaPny4NcxKkre3t6ZMmaLx48drw4YNmj59uh2rLL86NarsVeKVgv4ZRw+No4fG0UNj6J9x9NA4emgM/TOOHlYNTmlpaRZ7F2FLs2fP1sKFC7Vq1SoNGzaswL7U1FTdfvvt6t69uzZv3mynCgEAAAAAtlDlfiyRmJgoSWrWrFmhfX5+fvL09FRSUlJllwUAAAAAsLEqF2jT09MlSV5eXkXur1WrlnUMAAAAAMC8qlygBQAAAADcHKpcoM2fmS1uFva3334rdvYWAAAAAGAeVS7Q5t87m38v7e+lpqYqIyNDgYGBlV0WAAAAAMDGqlyg7dKliyQpNja20L5t27YVGAMAAAAAMK8q99ie3Nxc3X333Tp37py2bt1qfRbtpUuX1Lt3byUnJ+urr75SkyZN7FwpAAAAAMCIKjdDW61aNb322mvKy8tTSEiIJk2apL///e/q2rWrTpw4oVmzZhFmy+jgwYN64IEH5O/vrwYNGqhPnz7atGmTvcsyjY0bNyo8PFw9evRQvXr15OPjo/Xr19u7LNM4e/asli9frj//+c8KCgpS3bp11aJFC40cOVIHDhywd3kOLysrS88995z69++vli1bys/PTy1atFC/fv30zjvvKCcnx94lmlZUVJR8fHzk4+Ojr776yt7lOLzg4GBrv/74KyQkxN7lmcaWLVs0ZMgQBQQEyM/PT23atNHYsWOVkpJi79Ic2vr164t9/+X/GjRokL3LdHgWi0WbN2/WwIEDdfvtt6t+/fq6++67FR4erh9//NHe5Tm8vLw8vfHGG+revbvq16+vxo0bq3///oqJibF3aaZWzd4FVITu3bvr888/V2RkpDZt2qScnBy1atVKL774ooYOHWrv8kxl586dGjZsmNzc3DR06FB5enpq8+bNGj16tFJSUvT000/bu0SH9/LLL+v06dOqU6eO/Pz8dPr0aXuXZCpvvPGGoqKiFBAQoJ49e8rX11eJiYmKjo5WdHS03nzzTf5elyAzM1NvvfWW2rdvr759+8rX11dpaWnaunWrJkyYoI8//lgffvihnJ2r3M83K9SxY8cUGRkpDw8PZWZm2rsc0/Dy8lJYWFih7f7+/naoxlwsFosmT56s1atXKyAgQMOGDZOnp6fOnTun3bt36/Tp02rUqJG9y3RYwcHBmj59epH7Nm/erP/+97/q3bt3JVdlPjNnztSyZct06623KiQkRLVq1dK3336rNWvW6KOPPtK///1vtWrVyt5lOiSLxaLHHntMmzdvVkBAgEaMGKHs7GzFxMTokUce0T/+8Q89/vjj9i7TlKrcJcewndzcXN1zzz06e/ZssZdvHzhwgP+I3MD27dsVGBgof39/LVq0SC+++KKWLVum4cOH27s0U9i8ebNuueUWde3atcD2+Ph4DR48WB4eHvr+++9Vo0YNO1Xo2PLy8pSbmytXV9cC23NzczVkyBDt2rVLGzduVL9+/exUofnk5OSoT58+ql69ugIDA/X+++9r69atuueee+xdmkMLDg6WJB05csTOlZjTihUr9Oyzz2rcuHGaO3euXFxcCuzPzc1VtWpVcp6iQmVnZ6tly5ZKT0/XsWPHVK9ePXuX5LBSU1N1xx13qGHDhtq1a5e8vb2t+5YtW6a///3vGj58uJYtW2bHKh3XJ598olGjRqljx47atGmTatasKUm6cOGCevTooZ9//ln79+/nStJy4EfyKNbOnTt18uRJhYaGWsOsJHl7e2vKlCnKzs7Whg0b7FihOfTo0YPQb8CgQYMKhVlJ6ty5s7p166a0tDQdO3bMDpWZg7Ozc6EwK12/PWPgwIGSpKSkpMouy9Tmz5+v7777TkuXLi0UKoCKcOXKFc2dO1dNmzbVnDlzinzfEWbLJzo6Wr/++qv69etHmL2B5ORk5eXlqWPHjgXCrCTdf//9kqTz58/bozRTiI6OliRNmTLFGmYlqU6dOho/fryuXr3KLWnlxL9+KNauXbskSb169Sq0L/+ynN27d1dqTcDvVa9eXZIIFeWQl5dnXfmdy8NK79ChQ1qwYIGee+45tWzZ0t7lmE52drbWr1+vn376SbVq1VL79u11991327sshxcbG6u0tDQNHz5c165dU0xMjBITE+Xt7a0ePXrwOEID1q5dK0l69NFH7VyJ42vWrJlcXV21d+9epaeny8vLy7rv888/lyTde++99irP4f3888+SVOQMbP62uLi4Sq2pqiDQolj5z/LNf7bv7/n5+cnT05OZHdjN6dOntX37dt16661q3bq1vctxeNnZ2VqwYIEsFosuXryoHTt26Pjx4xo+fDj/ASmlq1evKiwsTMHBwZo0aZK9yzGl1NRUPfXUUwW2tW/fXqtWrVJAQICdqnJ8hw4dknT9h3ddunTRiRMnrPucnZ01fvx4vfzyy3aqzrySk5O1Y8cONWzYUH369LF3OQ7vlltu0QsvvKCZM2fq//7v/zRgwADrPbQ7d+7UuHHjuAe0BHXq1JEknTp1SrfffnuBfadOnZKkAn+3UXoEWhQrPT1dkgr8BO73atWqZR0DVKacnBw98cQTunr1qiIiIpihLYXs7GzNnTvX+rGTk5OefvppvfDCC3asylxeffVVJSYmavv27bznymH48OHq1KmTWrVqJQ8PD504cULLli3Txo0bNWjQIMXHx6tWrVr2LtMh5V/GuWzZMrVt21axsbFq0aKFEhISFB4erqVLlyogIEBjx461c6Xmsn79euXl5enhhx/m73QpPfXUU2rQoIEmTpyot956y7q9U6dOCg0N5dL3EvTp00cfffSRFi1apO7du8vNzU2S9Ouvv2rFihWSrq9Tg7LjHloAppKXl6fx48crPj5eo0aN0kMPPWTvkkzB09NTaWlp+vXXX3X06FHNnz9fa9eu1cCBA/nBVCns379fS5Ys0TPPPMMl2uU0Y8YM3Xvvvapbt67c3d3Vpk0brVy5Un/5y190+vRprVmzxt4lOqy8vDxJkqurq9avX6/27dvL09NTnTt31urVq+Xs7KylS5fauUpzycvL0/r16+Xk5KQRI0bYuxzTmDt3rh5//HFNmTJFR48eVUpKij777DNlZWVp4MCBPH6mBA888IC6deumPXv2qHPnzpo2bZomT56sjh07Wn+YxxMHyoeuoVj5M7PF/Wf3t99+K3b2FqgIeXl5euqpp/TBBx/owQcf1KJFi+xdkuk4OzurYcOGGjt2rBYvXqy9e/dqwYIF9i7LoeXm5iosLEytW7fW5MmT7V1OlTN69GhJ0r59++xciePK/17brl071a9fv8C+Vq1aqWnTpjp58qTS0tLsUJ05bd++XSkpKerevbuaNm1q73JMYfv27YqMjNRf//pXTZ48WQ0bNpSnp6c6deqk9957T9WrV9fMmTPtXabDqlatmj788EPNmDFDzs7OWrNmjbZs2aIBAwZY7+X29fW1c5XmxHUBKFb+vbOJiYlq165dgX2pqanKyMhQ+/bt7VAZbkb5M7PvvfeeQkNDtWLFCn6SaVDPnj0l/W8BOBQtIyPDuqZA3bp1ixxz3333SZLeeecd6+rRKJ38+8ouX75s50ocV/PmzSWp0Mqy+fK3Z2VlVVpNZsdiUGW3detWSVK3bt0K7fPz81Pz5s2VkJCgjIwMeXp6VnZ5plCjRg3NmDFDM2bMKLA9fzGoO++80x5lmR6BFsXq0qWLFi5cqNjYWA0bNqzAvvzVUbt06WKP0nCT+X2YHTp0qFauXMn9Tjbw008/SfrfatEoWo0aNTRy5Mgi98XHxysxMVH9+/eXr68vj+gqhwMHDkgSvStBfoA4fvx4oX05OTlKSkqSh4cHszul9OuvvyomJka1a9fmB1BlkJ2dLan4R/NcuHBBzs7OfE8phw8++ECSCv1/G6VDoEWx7r33XjVt2lQffvihnnjiCeuzaC9duqSFCxfK1dWV+xdR4fIvM37vvfc0ZMgQvfHGG4TZMvjuu+/k7+8vd3f3AtsvX76sv//975L+N7uIotWsWVNLliwpcl9YWJgSExM1ZcoU3XPPPZVcmXkcP35cjRo1KvQ+PH78uCIiIiRJoaGhdqjMHAICAtSrVy/FxsZq7dq1BWYVFy1apEuXLunBBx9kQZ5Seu+995Sdna3Ro0erRo0a9i7HNDp27Kh//vOfWr58uQYNGlTgioG33npLZ86cUceOHelpCf74uCNJ+uSTT/TOO++offv2+tOf/mSnysyNf/lQrGrVqum1117TsGHDFBISoqFDh8rT01ObN2/W6dOn9dJLLxX5LC0UtHbtWu3Zs0eSdOzYMUnSunXrrJd5durUiUueSjB37lxt2LBBnp6euu222zRv3rxCY0JCQqw/cEFBmzZt0vLly9WxY0f5+/urVq1aOnv2rL744gv9+uuv6tSpk8aPH2/vMlHFffTRR1q+fLk6d+6sxo0by93dXSdOnNDWrVuVk5OjKVOmcMXPDSxYsEB9+/bVxIkTFR0dbb28c+fOnWrcuLFeeukle5doGu+8844kLjcuqyFDhmjVqlWKj4/X3Xffrf79+8vb21uHDx/Wzp07VbNmTb3yyiv2LtOh9enTRw0bNlSLFi3k5uamr7/+Wrt27VLTpk21evVqfmBfTgRalKh79+76/PPPFRkZqU2bNiknJ0etWrXSiy++qKFDh9q7PFPYs2ePNmzYUGDb3r17tXfvXuvHfFMtXnJysqTr9zHOnz+/yDH+/v4E2mLcf//9+umnn7R//37t379fmZmZ8vLyUuvWrTVs2DCNGDGCWR1UuG7duun48eNKSEjQnj17dPnyZdWpU0f33Xefxo0bp169etm7RIcXEBCgL7/8Uq+++qq2bdum2NhY+fn56a9//av+9re/FXt/Nwr6+uuvdezYMd111108w7yMXFxcrD8k3bRpkz788ENlZ2erXr16evDBBzV16tRCz1dFQX/+85+1ZcsWHThwQDk5OWrSpImeeeYZTZw4kYVWDXBKS0uz2LsIAAAAAADKiiVCAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAAAAAJgSgRYAAAAAYEoEWgAAAACAKRFoAQAAAACmRKAFAMBkgoOD5ePjo7i4OJu9ZmRkpHx8fBQWFmaz1wQAoKJVs3cBAADcTEJCQrR79+4C22rUqCEvLy/Vq1dPbdq0Ubdu3TRkyBC5u7vbqcqCIiMjJUlhYWHy8fGxbzEAAPwOgRYAADto1KiRGjVqJEnKzc1Venq6EhMTdfToUW3YsEEzZsxQRESExowZU+jYgIAAubm52TTw1qlTR82bN9ett95aaN/cuXMlSY888giBFgDgUJzS0tIs9i4CAICbRf4M7fTp0/Xss88W2JeTk6P9+/dr6dKl+uyzzyRJTz75pObMmWOPUq3yQ+zhw4fVpEkTu9YCAMDvcQ8tAAAOonr16urSpYs2bNig559/XpL0+uuva8uWLXauDAAAx0SgBQDAAU2ZMkU9evSQ9L9LfvPdaFGojz/+WP369VPDhg3l7++vkJAQff7555Kuz7b6+Pjo1KlTBY4palGo/G352rZtaz3ex8fHem8tAAD2wj20AAA4qCeeeELbt2/Xt99+q9OnT6tx48Y3POb555/Xa6+9JkmqW7euGjdurO+++04PPfRQoWB8I40aNVLHjh21d+9eSdKdd96pGjVqFNgPAIA9EWgBAHBQnTp1kpOTkywWi7766qsbBtr//Oc/eu211+Tk5KRXX31VTzzxhJydnXXt2jVFRUVp1qxZZTr/yJEjNXLkSOss7erVq7mHFgDgULjkGAAAB+Xj46NatWpJkn7++ecbjo+KipIkjRgxQmFhYXJ2vv5t3sXFRVOnTtXAgQMrrFYAAOyBQAsAgAPz9PSUJGVkZJQ4LiMjw3pp8KOPPlrkmFGjRtm2OAAA7IxACwCAA8sPsl5eXiWOS0pKUl5enqTri0YVpW3btrYtDgAAOyPQAgDgoC5evKj09HRJUr169Uocm5mZKUmqVq2a3NzcihyTP9sLAEBVQaAFAMBBxcfHW/98zz33lDjWw8NDkpSbm6usrKwix9zosmUAAMyGQAsAgINauXKlpOuXCjds2LDEsYGBgdZFoL799tsixyQkJNi2QAAA7IxACwCAA1q4cKF27twpSZo+ffoNx3t6eqpjx46SpHXr1hU5Zu3ateWqxd3dXZJ05cqVch0PAEBFIdACAOAgcnNzFR8fr4cfflizZ8+WJE2YMEEDBgwo1fHh4eGSrgfXlStXWheJunbtmhYvXqzNmzeXq66AgABJ0vbt28t1PAAAFaWavQsAAOBmtH79eu3YsUPS9cCZnp6u5ORk6yyot7e3Zs+eXaZH7fTt21cTJ07Ua6+9punTp2vBggVq3LixTp06pfPnz2vOnDmaMWOGpOvPpi2thx56SLNmzdKMGTP01ltvydfXV05OTnrkkUc0fPjwMnzWAADYFoEWAAA7SElJUUpKiiTJ1dVVXl5eCgwMVJs2bdS9e3cNGTJENWvWLPPrzp49W+3atdOKFSt09OhR/fDDD2rdurUWL16se++91xpoa9WqVerXfOqppyRJGzduVFJSko4fPy5J6tq1a5nrAwDAlpzS0tIs9i4CAABUvIMHD6pXr16qXbu2Tp48ae9yAAAwjHtoAQC4SeQvCtWpUyc7VwIAgG0QaAEAqEJWrVql+Ph4WSz/uwArKytL8+fP15o1ayRJjz/+uL3KAwDApriHFgCAKmTHjh2aOnWqfHx8FBAQoGvXrunEiRO6fPmyJGnKlCnq0aOHfYsEAMBGCLQAAFQhjz32mGrUqKEDBw4oMTFRV65c0S233KIePXpo7Nix6t27t71LBADAZlgUCgAAAABgStxDCwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATIlACwAAAAAwJQItAAAAAMCUCLQAAAAAAFMi0AIAAAAATOn/AcQqVS6U0doTAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
diff --git a/src/micrograd_pp/__init__.py b/src/micrograd_pp/__init__.py
index 3f54508..75f4563 100644
--- a/src/micrograd_pp/__init__.py
+++ b/src/micrograd_pp/__init__.py
@@ -1,10 +1,11 @@
 from ._expr import Constant, Expr, Parameter, is_grad_enabled, maximum, no_grad, relu
-from ._nn import Linear, ReLU, Sequential
+from ._nn import BatchNorm1d, Linear, ReLU, Sequential, eval, is_eval
 from ._opt import SGD
 
 from . import datasets
 
 __all__ = (
+    "BatchNorm1d",
     "Constant",
     "Expr",
     "Linear",
@@ -13,6 +14,8 @@
     "Sequential",
     "SGD",
     "datasets",
+    "eval",
+    "is_eval",
     "is_grad_enabled",
     "maximum",
     "no_grad",
diff --git a/src/micrograd_pp/_expr.py b/src/micrograd_pp/_expr.py
index 68e1304..0ee7d90 100644
--- a/src/micrograd_pp/_expr.py
+++ b/src/micrograd_pp/_expr.py
@@ -10,6 +10,8 @@
 import numpy as np
 import numpy.typing as npt
 
+from ._util import n_samples
+
 
 _grad_mode = True
 
@@ -222,6 +224,21 @@ def max(self, dim: int | tuple[int, ...] | None = None, keepdim: bool = False) -
             retval = _Squeeze(retval, dim=dim)
         return retval
 
+    def mean(self, dim: int | tuple[int, ...] | None = None, keepdim: bool = False) -> Expr:
+        """Mean across one or more dimensions.
+
+        Parameters
+        ----------
+        dim
+            Axis or axes along which to operate. By default, all axes are used.
+        keepdim
+            Whether the output retains the specified dimension(s)
+        """
+        retval = _Sum(self, dim=dim) / n_samples(dim=dim, shape=self.shape)
+        if not keepdim:
+            retval = _Squeeze(retval, dim=dim)
+        return retval
+
     def set_label(self, label: str) -> None:
         """Set the expression label."""
         self._label = label
@@ -288,6 +305,22 @@ def unsqueeze(self, dim: int) -> Expr:
         """
         return _Unsqueeze(self, dim=dim)
 
+    def var(self, dim: int | tuple[int, ...] | None = None, keepdim: bool = False) -> Expr:
+        """Variance across one or more dimensions.
+
+        Parameters
+        ----------
+        dim
+            Axis or axes along which to operate. By default, all axes are used.
+        keepdim
+            Whether the output retains the specified dimension(s)
+        """
+        delta = self - self.mean(dim=dim, keepdim=True)
+        retval = (delta * delta).mean(dim=dim, keepdim=True)
+        if not keepdim:
+            retval = _Squeeze(retval, dim=dim)
+        return retval
+
     @property
     def dtype(self) -> npt.DTypeLike:
         """Data type."""
diff --git a/src/micrograd_pp/_nn.py b/src/micrograd_pp/_nn.py
index 12466f8..44c919d 100644
--- a/src/micrograd_pp/_nn.py
+++ b/src/micrograd_pp/_nn.py
@@ -1,14 +1,116 @@
+import contextlib
 from collections.abc import Callable
-from typing import Any
+from typing import Any, Generator
 
 import numpy as np
 
-from ._expr import Expr, Parameter, relu
+from ._expr import Constant, Expr, Parameter, relu
+from ._util import n_samples
 
 
 Module = Callable[[Expr], Expr]
 
 
+_eval_mode = False
+
+
+@contextlib.contextmanager
+def eval() -> Generator[None, None, None]:
+    """Context manager to switch to eval mode."""
+    global _eval_mode
+    state = _eval_mode
+    _eval_mode = True
+    yield
+    _eval_mode = state
+
+
+def is_eval() -> bool:
+    """Determines whether or not eval mode is enabled."""
+    return _eval_mode
+
+
+class BatchNorm1d:
+    """Batch normalization.
+
+    Parameters
+    ----------
+    num_features
+        Number of features
+    affine
+        Whether to use learnable scale and shift parameters
+    dtype
+        Data type for running mean and variance and scale and shift parameters
+    eps
+        When standardizing, this quantity is added to the denominator for numerical stability
+    momentum
+        Momentum used for the running mean and variance computations (if None, an ordinary average is computed)
+    track_running_stats
+        Whether to keep a running mean and variance
+    """
+
+    def __init__(
+        self,
+        num_features: int,
+        affine: bool = True,
+        dtype: type = np.float32,
+        eps: float = 1e-5,
+        momentum: float | None = 0.1,
+        track_running_stats: bool = True,
+    ) -> None:
+        self._eps = eps
+        self._momentum = momentum
+        self._num_features = num_features
+        if track_running_stats:
+            self._running_mean = np.zeros((num_features,), dtype=dtype)
+            self._running_var = np.ones((num_features,), dtype=dtype)
+        else:
+            self._running_mean = None
+            self._running_var = None
+        if affine:
+            self._scale = Parameter(np.ones((num_features,), dtype=dtype))
+            self._shift = Parameter(np.zeros((num_features,), dtype=dtype))
+        else:
+            self._scale = None
+            self._shift = None
+        self._n = 0
+
+    def __call__(self, x: Expr) -> Expr:
+        dim = (0,) + tuple(range(2, x.ndim))
+        if self._running_mean is not None and self._running_var is not None and is_eval():
+            mean = Constant(self._running_mean)
+            var = Constant(self._running_var)
+        else:
+            mean = x.mean(dim=dim)
+            var = x.var(dim=dim)
+        if self._running_mean is not None and self._running_var is not None:
+            increment = n_samples(dim, x.shape)
+            n_new = self._n + increment
+            if self._momentum is None:
+                a = self._n / n_new
+                b = increment / n_new
+            else:
+                a = 1.0 - self._momentum
+                b = self._momentum
+            self._running_mean = a * self._running_mean + b * mean.value
+            self._running_var = a * self._running_var + b * var.value
+            self._n = n_new
+        shape = (1, x.shape[1]) + ((1,) * (x.ndim - 2))
+        mean = mean.expand(shape)
+        var = var.expand(shape)
+        x_norm = (x - mean) / ((var + self._eps) ** 0.5)
+        if self._scale is not None and self._shift is not None:
+            return self._scale * x_norm + self._shift
+        else:
+            return x_norm
+
+    def __repr__(self) -> str:
+        return (
+            f"BatchNorm1d({self._num_features}, x={self._eps=}, momentum={self._momentum}, "
+            f"affine={self._scale is not None and self._shift is not None}, "
+            f"track_running_stats={self._running_mean is not None and self._running_var is not None})"
+        )
+
+
 class Linear:
     """Linear layer.
 
diff --git a/src/micrograd_pp/_util.py b/src/micrograd_pp/_util.py
new file mode 100644
index 0000000..f396e34
--- /dev/null
+++ b/src/micrograd_pp/_util.py
@@ -0,0 +1,9 @@
+import numpy as np
+
+
+def n_samples(dim: int | tuple[int, ...] | None, shape: tuple[int, ...]) -> int:
+    if isinstance(dim, int):
+        return shape[dim]
+    if dim is None:
+        return np.prod(shape).item()
+    return np.prod([shape[d] for d in dim]).item()
diff --git a/tests/test_expr.py b/tests/test_expr.py
index 462e4f4..d175441 100644
--- a/tests/test_expr.py
+++ b/tests/test_expr.py
@@ -113,6 +113,17 @@ def test_maximum() -> None:
     np.testing.assert_equal(b_.grad, ~grad)
 
 
+@pytest.mark.parametrize("dim", DIMS)
+def test_mean(dim: int | tuple[int, ...] | None) -> None:
+    dim = (0, 2)
+    a = np.random.randn(4, 3, 2)
+    a_ = mpp.Parameter(a)
+    b_ = a_.mean(dim=dim)
+    b_.backward()
+    grad = np.ones_like(a) / np.ones_like(a).sum(axis=dim, keepdims=True)
+    np.testing.assert_equal(a_.grad, grad)
+
+
 def test_mult() -> None:
     a = np.random.randn(4, 1, 2)
     b = np.random.randn(3, 2)
@@ -205,3 +216,12 @@ def test_unsqueeze(dim: int) -> None:
     b_.backward()
     grad = np.ones_like(a)
     np.testing.assert_equal(a_.grad, grad)
+
+
+@pytest.mark.parametrize("dim", DIMS)
+def test_var(dim: int | tuple[int, ...] | None) -> None:
+    a = np.random.randn(4, 3, 2)
+    b = a.var(axis=dim)
+    a_ = mpp.Constant(a)
+    b_ = a_.var(dim=dim)
+    np.testing.assert_allclose(b_.value, b)
diff --git a/tests/test_mnist.py b/tests/test_mnist.py
index 58315df..8180500 100644
--- a/tests/test_mnist.py
+++ b/tests/test_mnist.py
@@ -46,6 +46,7 @@ def test_mnist(batch_sz: int = 64, n_epochs: int = 3):
     # Feedforward neural network
     model = mpp.Sequential(
         mpp.Linear(28 * 28, 128),
+        mpp.BatchNorm1d(128),
         mpp.ReLU(),
         mpp.Linear(128, 10),
     )
@@ -60,7 +61,7 @@ def test_mnist(batch_sz: int = 64, n_epochs: int = 3):
             loss.backward(opt=opt)
             opt.step()
         test_x = mpp.Constant(test_images)
-        with mpp.no_grad():
+        with mpp.eval(), mpp.no_grad():
             test_fx = model(test_x)
         pred_labels = np.argmax(test_fx.value, axis=1)
         accuracy = (pred_labels == test_labels).mean().item()
diff --git a/tests/test_nn.py b/tests/test_nn.py
new file mode 100644
index 0000000..23672e9
--- /dev/null
+++ b/tests/test_nn.py
@@ -0,0 +1,54 @@
+from typing import Generator
+
+import numpy as np
+import pytest
+
+import micrograd_pp as mpp
+
+BATCH_SZ = 64
+NUM_FEATURES = 10
+
+
+@pytest.fixture(autouse=True)
+def run_before_and_after_tests() -> Generator[None, None, None]:
+    np.random.seed(0)
+    yield
+
+
+@pytest.mark.parametrize("momentum", [0.1, None])
+def test_batch_norm_1d_track_running_stats(momentum: float) -> None:
+    num_iters = 1_000
+    shift = np.random.randn(10)
+    scale = np.random.randn(10)
+    bn = mpp.BatchNorm1d(NUM_FEATURES, affine=False, momentum=momentum)
+    for _ in range(num_iters):
+        x = scale * np.random.randn(BATCH_SZ, NUM_FEATURES) + shift
+        x_ = mpp.Constant(x)
+        bn(x_)
+    assert bn._running_mean is not None
+    assert bn._running_var is not None
+    np.testing.assert_allclose(bn._running_mean, shift, atol=0.1, rtol=0.0)
+    np.testing.assert_allclose(bn._running_var, scale * scale, atol=0.1, rtol=0.0)
+
+
+def test_batch_norm_1d_standardize() -> None:
+    shift = np.random.randn(10)
+    scale = np.random.randn(10)
+    bn = mpp.BatchNorm1d(NUM_FEATURES, affine=False)
+    x = scale * np.random.randn(BATCH_SZ, NUM_FEATURES) + shift
+    x_ = mpp.Constant(x)
+    y_ = bn(x_)
+    np.testing.assert_allclose(y_.value.mean(axis=0), 0.0, atol=1e-6, rtol=0.0)
+    np.testing.assert_allclose(y_.value.var(axis=0), 1.0, atol=1e-3, rtol=0.0)
+
+
+def test_batch_norm_1d_eval() -> None:
+    shift = np.random.randn(10)
+    scale = np.random.randn(10)
+    bn = mpp.BatchNorm1d(NUM_FEATURES, affine=False)
+    x = scale * np.random.randn(BATCH_SZ, NUM_FEATURES) + shift
+    x_ = mpp.Constant(x)
+    with mpp.eval():
+        y_ = bn(x_)
+        # The input should be close to the output since the batch norm scale and shift are 1 and 0 at initialization
+        np.testing.assert_allclose(x_.value, y_.value, atol=1e-4, rtol=0.0)