hunk.code_window:     assert not torch.isnan(outputs).any(), 'Output included NaNs'
   @pytest.mark.timeout(120)
   @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
   @pytest.mark.parametrize('batch_size', [2])
   def test_model_backward(model_name, batch_size):
       """Run a single forward pass with each model"""
  
hunk.labels: keep keep keep keep replace keep keep keep
hunk.after_edit: @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
  
hunk.file_path: tests/test_models.py
hunk.type: replace
file: # NASNet
  
  **NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells.
  
  {% include 'code_snippets.md' %}
  
  ## How do I train this model?
  
  You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
  
  ## Citation
  
  ```BibTeX
  @misc{zoph2018learning,
        title={Learning Transferable Architectures for Scalable Image Recognition}, 
        author={Barret Zoph and Vijay Vasudevan and Jonathon Shlens and Quoc V. Le},
        year={2018},
        eprint={1707.07012},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: NASNet
    Paper:
      Title: Learning Transferable Architectures for Scalable Image Recognition
      URL: https://paperswithcode.com/paper/learning-transferable-architectures-for
  Models:
  - Name: nasnetalarge
    In Collection: NASNet
    Metadata:
      FLOPs: 30242402862
      Parameters: 88750000
      File Size: 356056626
      Architecture:
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Depthwise Separable Convolution
      - Dropout
      - ReLU
      Tasks:
      - Image Classification
      Training Techniques:
      - Label Smoothing
      - RMSProp
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 50x Tesla K40 GPUs
      ID: nasnetalarge
      Dropout: 0.5
      Crop Pct: '0.911'
      Momentum: 0.9
      Image Size: '331'
      Interpolation: bicubic
      Label Smoothing: 0.1
      RMSProp $\epsilon$: 1.0
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/nasnet.py#L562
    Weights: http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 82.63%
        Top 5 Accuracy: 96.05%
  -->
  