Task: Data-Imputation (source dataset: tablib)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| dev       | name                              |   batch_size |   speedup |   abs_latency |   compilation_latency |   compression_ratio |
|:----------|:----------------------------------|-------------:|----------:|--------------:|----------------------:|--------------------:|
| cuda      | BERT_pytorch                      |           16 |    2.6028 |       22.2879 |               41.0046 |              1.1965 |
| cuda      | Background_Matting                |            4 |    1.1296 |      112.763  |               27.8916 |              1.0396 |
| cuda      | LearningToPaint                   |           96 |    1.0951 |       11.3205 |               13.0241 |              0.996  |
| cuda      | Super_SloMo                       |            6 |    1.216  |       65.3294 |               27.1633 |              1.2396 |
| cuda      | alexnet                           |          128 |    1.1919 |        8.2399 |                6.5561 |              1.0008 |
| cuda      | attention_is_all_you_need_pytorch |          256 |    1.4975 |       36.6682 |               43.061  |              1.1824 |
| cuda      | dcgan                             |           32 |    0.9276 |        2.2476 |                5.7151 |              1.0064 |
| cuda      | demucs                            |            4 |    1.0313 |       51.7716 |               12.8195 |              0.9971 |
| cuda      | densenet121                       |            4 |    1.1976 |       46.0111 |               64.0118 |              0.9945 |
| cuda      | dlrm                              |         1024 |    1.3421 |        3.2177 |                4.9493 |              1.0009 |
| cuda      | drq                               |            1 |    1.082  |        3.8157 |                8.0732 |              0.9687 |
| cuda      | fastNLP_Bert                      |            6 |    1.4839 |       37.905  |               32.7583 |              1.1563 |
| cuda      | functorch_dp_cifar10              |           64 |    1.5014 |        6.9596 |               14.1516 |              0.4432 |
| cuda      | hf_Albert                         |            8 |    2.2452 |       30.6134 |               25.9036 |              1.1941 |
| cuda      | hf_Bart                           |            4 |    1.7012 |       34.3999 |               37.9975 |              1.0128 |
| cuda      | hf_Bert                           |            4 |    1.9003 |       23.3435 |               34.8196 |              1.0273 |
| cuda      | hf_Bert_large                     |            4 |    1.6346 |       52.8525 |               62.3112 |              1.0726 |
| cuda      | hf_BigBird                        |            2 |    1.9208 |      105.267  |              101.479  |              1.1415 |
| cuda      | hf_DistilBert                     |            8 |    1.3988 |       22.5793 |               20.2386 |              1.0232 |
| cuda      | hf_GPT2                           |            4 |    1.8075 |       27.5184 |               25.3428 |              1.1562 |
| cuda      | hf_GPT2_large                     |            4 |    1.7716 |      118.74   |               68.1618 |              1.1725 |
| cuda      | hf_Reformer                       |            4 |    1.1744 |       70.4228 |               15.1152 |              0.9266 |
| cuda      | hf_T5                             |            8 |    1.8778 |       93.3134 |               37.0046 |              1.2279 |
| cuda      | hf_T5_large                       |            2 |    2.3623 |      101.552  |              143.798  |              1.1674 |
| cuda      | lennard_jones                     |         1000 |    1.0649 |        1.5233 |                4.1119 |              0.9998 |
| cuda      | mnasnet1_0                        |           32 |    1.1957 |       19.1993 |               27.2302 |              0.7758 |
| cuda      | mobilenet_v2                      |           96 |    1.4876 |       32.3311 |               27.4719 |              1.1729 |
| cuda      | mobilenet_v3_large                |           32 |    1.3051 |       21.0818 |               55.7101 |              0.7771 |
| cuda      | nvidia_deeprecommender            |          256 |    1.0182 |       10.0515 |                5.1433 |              0.9711 |
| cuda      | phlippe_densenet                  |          128 |    1.123  |       21.9244 |               26.8021 |              1.0062 |
| cuda      | phlippe_resnet                    |          128 |    1.0857 |        8.8702 |               11.5935 |              1.0037 |
| cuda      | pytorch_CycleGAN_and_pix2pix      |            1 |    1.8336 |        7.4113 |               13.1523 |              1.0224 |
| cuda      | pytorch_stargan                   |           16 |    1.2906 |       11.6881 |               45.2834 |              0.8874 |
| cuda      | pytorch_struct                    |          200 |    1.2499 |        3.9393 |               18.4688 |              0.7357 |
| cuda      | pytorch_unet                      |            1 |    1.3525 |       29.6253 |               14.6794 |              1.0087 |
| cuda      | resnet152                         |           32 |    1.0883 |       60.3646 |               65.7002 |              0.9385 |
| [MISSING] | resnet18                          |           16 |    0.9888 |       10.3945 |               15.6529 |              0.619  |
| cuda      | resnet50                          |           32 |    1.1437 |       23.2979 |               27.0392 |              0.8824 |
| cuda      | resnext50_32x4d                   |            8 |    1.0935 |       19.048  |               27.195  |              0.7721 |
| cuda      | shufflenet_v2_x1_0                |          128 |    1.3027 |       25.7017 |               27.9875 |              1.1015 |
| cuda      | soft_actor_critic                 |          256 |    0.9965 |        2.258  |                4.6661 |              0.9995 |
| cuda      | speech_transformer                |           32 |    1.8405 |       35.1645 |               33.3422 |              1.0888 |
| cuda      | squeezenet1_1                     |           32 |    1.4191 |        7.3454 |                9.4751 |              1.1148 |
| cuda      | timm_efficientdet                 |            1 |    1.663  |       78.2697 |              150.962  |              0.9904 |
| cuda      | timm_efficientnet                 |           32 |    1.2689 |       28.5348 |               66.3911 |              0.9428 |
| cuda      | timm_nfnet                        |          128 |    1.5319 |       79.5429 |               32.9961 |              1.107  |
| cuda      | timm_regnet                       |           32 |    1.0564 |       56.9897 |               53.0027 |              0.95   |
| cuda      | timm_resnest                      |           32 |    1.6485 |       14.3908 |               56.724  |              0.9515 |
| cuda      | timm_vision_transformer           |            8 |    1.61   |       18.7736 |               36.9495 |              0.7301 |
| cuda      | timm_vision_transformer_large     |            8 |    1.0842 |      170.985  |               72.0604 |              0.9762 |
| cuda      | timm_vovnet                       |           32 |    1.0472 |       25.4676 |               24.8428 |              0.8843 |
| cuda      | tts_angular                       |           64 |    1.0366 |        6.9889 |                4.2683 |              0.9973 |
| cuda      | vgg16                             |           64 |    1.256  |       52.7072 |                7.3733 |              0.9884 |
| cuda      | yolov3                            |           16 |    1.26   |       54.235  |               42.4711 |              1.0108 |



row_idx: 36
col_idx: 0
col_name: dev
label: cuda
output: value: cuda
tablib_key: tables/job=github_000005/batch=003598/fx_QUYVqlMDJkCJ9ZxNRQ6QLsm69kK3ViA5QxijuQAI=