hunk.code_window:     """
       def __init__(
               self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',
               embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
               drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='', class_token=True,
               fc_norm=None, embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
           """
           Args:
               img_size (int, tuple): input image size
  
hunk.labels: keep keep keep keep keep replace replace keep keep keep
hunk.after_edit:             class_token=True, fc_norm=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='',
               embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
  
hunk.file_path: timm/models/vision_transformer.py
hunk.type: replace
file: # (Tensorflow) EfficientNet Lite
  
  **EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales  these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$,  width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a  principled way.
  
  The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
  
  The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2).
  
  EfficientNet-Lite makes EfficientNet more suitable for mobile devices by introducing [ReLU6](https://paperswithcode.com/method/relu6) activation functions and removing [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation).
  
  The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('tf_efficientnet_lite0', pretrained=True)
  model.eval()
  ```
  
  To load and preprocess the image:
  ```python 
  import urllib
  from PIL import Image
  from timm.data import resolve_data_config
  from timm.data.transforms_factory import create_transform
  
  config = resolve_data_config({}, model=model)
  transform = create_transform(**config)
  
  url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
  urllib.request.urlretrieve(url, filename)
  img = Image.open(filename).convert('RGB')
  tensor = transform(img).unsqueeze(0) # transform and add batch dimension
  ```
  
  To get the model predictions:
  ```python
  import torch
  with torch.no_grad():
      out = model(tensor)
  probabilities = torch.nn.functional.softmax(out[0], dim=0)
  print(probabilities.shape)
  # prints: torch.Size([1000])
  ```
  
  To get the top-5 predictions class names:
  ```python
  # Get imagenet class mappings
  url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
  urllib.request.urlretrieve(url, filename) 
  with open("imagenet_classes.txt", "r") as f:
      categories = [s.strip() for s in f.readlines()]
  
  # Print top categories per image
  top5_prob, top5_catid = torch.topk(probabilities, 5)
  for i in range(top5_prob.size(0)):
      print(categories[top5_catid[i]], top5_prob[i].item())
  # prints class names and probabilities like:
  # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
  ```
  
  Replace the model name with the variant you want to use, e.g. `tf_efficientnet_lite0`. You can find the IDs in the model summaries at the top of this page.
  
  To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
  
  ## How do I finetune this model?
  You can finetune any of the pre-trained models just by changing the classifier (the last layer).
  ```python
  model = timm.create_model('tf_efficientnet_lite0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
  ```
  To finetune on your own dataset, you have to write a training loop or adapt [timm's training
  script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
  
  ## 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{tan2020efficientnet,
        title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, 
        author={Mingxing Tan and Quoc V. Le},
        year={2020},
        eprint={1905.11946},
        archivePrefix={arXiv},
        primaryClass={cs.LG}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: TF EfficientNet Lite
    Paper:
      Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
      URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
  Models:
  - Name: tf_efficientnet_lite0
    In Collection: TF EfficientNet Lite
    Metadata:
      FLOPs: 488052032
      Parameters: 4650000
      File Size: 18820223
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - RELU6
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: tf_efficientnet_lite0
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1596
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 74.83%
        Top 5 Accuracy: 92.17%
  - Name: tf_efficientnet_lite1
    In Collection: TF EfficientNet Lite
    Metadata:
      FLOPs: 773639520
      Parameters: 5420000
      File Size: 21939331
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - RELU6
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: tf_efficientnet_lite1
      Crop Pct: '0.882'
      Image Size: '240'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1607
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 76.67%
        Top 5 Accuracy: 93.24%
  - Name: tf_efficientnet_lite2
    In Collection: TF EfficientNet Lite
    Metadata:
      FLOPs: 1068494432
      Parameters: 6090000
      File Size: 24658687
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - RELU6
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: tf_efficientnet_lite2
      Crop Pct: '0.89'
      Image Size: '260'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1618
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.48%
        Top 5 Accuracy: 93.75%
  - Name: tf_efficientnet_lite3
    In Collection: TF EfficientNet Lite
    Metadata:
      FLOPs: 2011534304
      Parameters: 8199999
      File Size: 33161413
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - RELU6
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: tf_efficientnet_lite3
      Crop Pct: '0.904'
      Image Size: '300'
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1629
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.83%
        Top 5 Accuracy: 94.91%
  - Name: tf_efficientnet_lite4
    In Collection: TF EfficientNet Lite
    Metadata:
      FLOPs: 5164802912
      Parameters: 13010000
      File Size: 52558819
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - RELU6
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: tf_efficientnet_lite4
      Crop Pct: '0.92'
      Image Size: '380'
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1640
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 81.54%
        Top 5 Accuracy: 95.66%
  -->