update gen

generator.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "bae751d8",
    "metadata": {},
    "outputs": [],
@@ -26,30 +26,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "c0e30f61",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BPE Tokenizer loaded from tokenizer.json\n",
-      " - Vocab size: 8002\n",
-      " - BPE merges: 7888\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<tokenizer.Tokenizer at 0x7d2bbbafcb90>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tokenizer = Tokenizer(vocab_size=8000)\n",
     "tokenizer.load(\"tokenizer.json\")"
@@ -57,28 +37,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "db130c45",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training samples: 15671\n",
-      "Validation samples: 1684\n",
-      "\n",
-      "Sample input (first 70 chars):\n",
-      "[CLS:parallel_for] for (int ix = 1; ix < (N + 1); ix++)\n",
-      "{\n",
-      "  forces[ix] = forces[ix] * force_retention;\n",
-      "}\n",
-      "\n",
-      "Sample output:\n",
-      "omp parallel for\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_inputs, train_outputs = [], []\n",
     "val_inputs, val_outputs = [], []\n",
@@ -134,34 +96,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "d5747915",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Dataset shapes:\n",
-      "  Train: 15671 samples\n",
-      "  Val: 1684 samples\n",
-      "  Sample input tensor shape: torch.Size([500])\n",
-      "  Sample output tensor shape: torch.Size([100])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_dataset = OpenMPDataset(\n",
     "    train_inputs, train_outputs, tokenizer,\n",
-    "    max_input_len=500,\n",
-    "    max_output_len=100\n",
+    "    max_input_len=1500,\n",
+    "    max_output_len=300\n",
     ")\n",
     "\n",
     "val_dataset = OpenMPDataset(\n",
     "    val_inputs, val_outputs, tokenizer,\n",
-    "    max_input_len=500,\n",
-    "    max_output_len=100\n",
+    "    max_input_len=1500,\n",
+    "    max_output_len=300\n",
     ")\n",
     "\n",
     "print(f\"\\nDataset shapes:\")\n",
@@ -173,38 +122,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "5252d457",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "✓ Dataloaders ready!\n",
-      "  Train batches: 490\n",
-      "  Val batches: 53\n",
-      "\n",
-      "Sample batch structure:\n",
-      "  input shape: torch.Size([32, 500])\n",
-      "  output shape: torch.Size([32, 100])\n",
-      "  input_len shape: torch.Size([32])\n",
-      "  First sample input_len: 12\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_loader = DataLoader(\n",
     "    train_dataset,\n",
-    "    batch_size=32,\n",
+    "    batch_size=8,\n",
     "    shuffle=True,\n",
     "    pin_memory=True\n",
     ")\n",
     "\n",
     "val_loader = DataLoader(\n",
     "    val_dataset,\n",
-    "    batch_size=32,\n",
+    "    batch_size=8,\n",
     "    shuffle=False,\n",
     "    pin_memory=True\n",
     ")\n",
@@ -223,49 +155,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "11631bed",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model architecture:\n",
-      "Generator(\n",
-      "  (encoder): Encoder(\n",
-      "    (embedding): Embedding(8002, 128, padding_idx=0)\n",
-      "    (lstm): LSTM(128, 256, num_layers=2, batch_first=True, dropout=0.3, bidirectional=True)\n",
-      "    (dropout): Dropout(p=0.3, inplace=False)\n",
-      "    (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
-      "  )\n",
-      "  (decoder): Decoder(\n",
-      "    (attention): BahdanauAttention(\n",
-      "      (W1): Linear(in_features=512, out_features=256, bias=True)\n",
-      "      (W2): Linear(in_features=256, out_features=256, bias=True)\n",
-      "      (V): Linear(in_features=256, out_features=1, bias=True)\n",
-      "    )\n",
-      "    (embedding): Embedding(8002, 128, padding_idx=0)\n",
-      "    (lstm): LSTM(640, 256, num_layers=2, batch_first=True, dropout=0.3)\n",
-      "    (fc_out): Linear(in_features=896, out_features=8002, bias=True)\n",
-      "    (dropout): Dropout(p=0.3, inplace=False)\n",
-      "    (layer_norm): LayerNorm((896,), eps=1e-05, elementwise_affine=True)\n",
-      "  )\n",
-      "  (hidden_projection): Linear(in_features=512, out_features=256, bias=True)\n",
-      "  (cell_projection): Linear(in_features=512, out_features=256, bias=True)\n",
-      ")\n",
-      "\n",
-      "Total parameters: 13,502,531\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "VOCAB_SIZE = tokenizer.vocab_size\n",
     "EMBED_SIZE = 128\n",
     "HIDDEN_SIZE = 256\n",
-    "NUM_LAYERS = 2\n",
-    "DROPOUT = 0.3\n",
+    "NUM_LAYERS = 3\n",
+    "DROPOUT = 0.2\n",
     "\n",
     "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
     "\n",
@@ -282,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "2d3125a6",
    "metadata": {},
    "outputs": [],
@@ -300,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "794c40e7",
    "metadata": {},
    "outputs": [],
@@ -357,198 +257,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "d4bb0e92",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 01/25 | Time: 8m 1s | TF Ratio: 0.50\n",
-      "\tTrain Loss: 4.1408 | Val Loss: 3.8033 | Best Val: 3.8033 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 02/25 | Time: 7m 41s | TF Ratio: 0.45\n",
-      "\tTrain Loss: 3.0543 | Val Loss: 3.5220 | Best Val: 3.5220 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 03/25 | Time: 7m 40s | TF Ratio: 0.41\n",
-      "\tTrain Loss: 2.6443 | Val Loss: 3.2353 | Best Val: 3.2353 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 04/25 | Time: 7m 44s | TF Ratio: 0.36\n",
-      "\tTrain Loss: 2.3818 | Val Loss: 3.1132 | Best Val: 3.1132 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 05/25 | Time: 7m 42s | TF Ratio: 0.33\n",
-      "\tTrain Loss: 2.2041 | Val Loss: 2.9274 | Best Val: 2.9274 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 06/25 | Time: 7m 36s | TF Ratio: 0.30\n",
-      "\tTrain Loss: 2.0576 | Val Loss: 2.8356 | Best Val: 2.8356 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 07/25 | Time: 7m 41s | TF Ratio: 0.27\n",
-      "\tTrain Loss: 1.9377 | Val Loss: 2.8092 | Best Val: 2.8092 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 08/25 | Time: 7m 39s | TF Ratio: 0.24\n",
-      "\tTrain Loss: 1.8034 | Val Loss: 2.8102 | Best Val: 2.8092  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 09/25 | Time: 7m 39s | TF Ratio: 0.22\n",
-      "\tTrain Loss: 1.7125 | Val Loss: 2.7772 | Best Val: 2.7772 ✓ SAVED\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 10/25 | Time: 7m 38s | TF Ratio: 0.19\n",
-      "\tTrain Loss: 1.6454 | Val Loss: 2.8247 | Best Val: 2.7772  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                           \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 11/25 | Time: 7m 42s | TF Ratio: 0.17\n",
-      "\tTrain Loss: 1.5686 | Val Loss: 2.8969 | Best Val: 2.7772  \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                          \r"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      7\u001b[39m start_time = time.time()\n\u001b[32m      9\u001b[39m tf_ratio = \u001b[38;5;28mmax\u001b[39m(\u001b[32m0.1\u001b[39m, \u001b[32m0.5\u001b[39m * (\u001b[32m0.9\u001b[39m ** epoch))\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m train_loss = \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCLIP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtf_ratio\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     11\u001b[39m valid_loss = evaluate(model, val_loader, criterion)\n\u001b[32m     12\u001b[39m scheduler.step(valid_loss)\n",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mtrain\u001b[39m\u001b[34m(model, iterator, optimizer, criterion, clip, teacher_forcing_ratio)\u001b[39m\n\u001b[32m     14\u001b[39m trg = trg[\u001b[32m1\u001b[39m:].reshape(-\u001b[32m1\u001b[39m)\n\u001b[32m     16\u001b[39m loss = criterion(output, trg)\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m \u001b[43mloss\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     19\u001b[39m torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n\u001b[32m     21\u001b[39m optimizer.step()\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/projects/env/lib/python3.12/site-packages/torch/_tensor.py:630\u001b[39m, in \u001b[36mTensor.backward\u001b[39m\u001b[34m(self, gradient, retain_graph, create_graph, inputs)\u001b[39m\n\u001b[32m    620\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m    621\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[32m    622\u001b[39m         Tensor.backward,\n\u001b[32m    623\u001b[39m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[32m   (...)\u001b[39m\u001b[32m    628\u001b[39m         inputs=inputs,\n\u001b[32m    629\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m630\u001b[39m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mautograd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    631\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43minputs\u001b[49m\n\u001b[32m    632\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/projects/env/lib/python3.12/site-packages/torch/autograd/__init__.py:364\u001b[39m, in \u001b[36mbackward\u001b[39m\u001b[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[39m\n\u001b[32m    359\u001b[39m     retain_graph = create_graph\n\u001b[32m    361\u001b[39m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[32m    362\u001b[39m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[32m    363\u001b[39m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m364\u001b[39m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    365\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    366\u001b[39m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    367\u001b[39m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    368\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    369\u001b[39m \u001b[43m    \u001b[49m\u001b[43minputs_tuple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    370\u001b[39m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    371\u001b[39m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    372\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/projects/env/lib/python3.12/site-packages/torch/autograd/graph.py:865\u001b[39m, in \u001b[36m_engine_run_backward\u001b[39m\u001b[34m(t_outputs, *args, **kwargs)\u001b[39m\n\u001b[32m    863\u001b[39m     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[32m    864\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m865\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_execution_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[32m    866\u001b[39m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m    867\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[32m    868\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    869\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
-      "\u001b[31mKeyboardInterrupt\u001b[39m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "EPOCHS = 25\n",
     "CLIP = 1.0\n",
@@ -597,26 +309,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "6d9a8e25",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded checkpoint from best_model.pth (epoch 8)\n",
-      "Sample input (truncated): [CLS:reduction] for (i = 0; i < 1000; ++i)\n",
-      "{\n",
-      "  logic_and = logic_and && logics[i];\n",
-      "}\n",
-      "\n",
-      "Reference pragma: omp parallel for schedule(dynamic,1) private(i) reduction(&&:logic_and)\n",
-      "Model prediction: omp parallel for schedule(dynamic,1) private(i) reduction(&&:logic_and)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "\n",
+    "import sys\n",
+    "import pathlib\n",
+    "sys.path.append(str(pathlib.Path().resolve()))  # ensure local modules are importable\n",
     "import os\n",
     "\n",
     "checkpoint_path = \"best_model.pth\"\n",
@@ -631,8 +332,8 @@
     "SOS_IDX = tokenizer.char2idx['<SOS>']\n",
     "EOS_IDX = tokenizer.char2idx['<EOS>']\n",
     "\n",
+    "# Greedy baseline (kept for comparison)\n",
     "def greedy_generate(code_snippet: str, cls: str = \"parallel\", max_len: int = 80) -> str:\n",
-    "    \"\"\"Greedy decode a pragma for a single code snippet.\"\"\"\n",
     "    model.eval()\n",
     "    text = code_snippet if code_snippet.startswith(\"[CLS:\") else f\"[CLS:{cls}] {code_snippet}\"\n",
     "    input_ids = tokenizer.encode(text, max_length=500, add_special_tokens=True)\n",
@@ -665,13 +366,15 @@
     "\n",
     "    return tokenizer.decode(generated)\n",
     "\n",
+    "\n",
+    "\n",
     "# Quick sanity check on a validation example\n",
     "sample_input = val_inputs[18]\n",
     "reference = val_outputs[18]\n",
-    "prediction = greedy_generate(sample_input)\n",
+    "prediction_greedy = greedy_generate(sample_input)\n",
     "print(\"Sample input (truncated):\", sample_input[:140] + \"...\" if len(sample_input) > 140 else sample_input)\n",
     "print(\"Reference pragma:\", reference)\n",
-    "print(\"Model prediction:\", prediction)\n"
+    "print(\"Greedy prediction:\", prediction_greedy)"
    ]
   }
  ],