class_docstr: Image to Patch Embedding
  
  Class adapted from the implementation in https://github.com/MadryLab/smoothed-vit
  
  Original License stated above.
class_docstr_tok: ["Image","to","Patch","Embedding","Class","adapted","from","the","implementation","in","https",":","\/\/github.com\/MadryLab\/smoothed-vit","Original","License","stated","above","."]
docstr: Specifies the configuration for the convolutional layer.
  
  :param patch_size: The patch size used by the ViT.
  :param in_channels: Number of input channels.
  :param embed_dim: The embedding dimension used by the ViT.
docstr_tok: ["Specifies","the","configuration","for","the","convolutional","layer",".",":","param","patch_size",":","The","patch","size","used","by","the","ViT",".",":","param","in_channels",":","Number","of","input","channels",".",":","param","embed_dim",":","The","embedding","dimension","used","by","the","ViT","."]
code: def __init__(
      self,
      patch_size: int = 16,
      in_channels: int = 1,
      embed_dim: int = 768,
  ):
      """
      Specifies the configuration for the convolutional layer.
  
      :param patch_size: The patch size used by the ViT.
      :param in_channels: Number of input channels.
      :param embed_dim: The embedding dimension used by the ViT.
      """
      super().__init__()
      self.patch_size = patch_size
      self.in_channels = in_channels
      self.embed_dim = embed_dim
      self.proj: Optional[torch.nn.Conv2d] = None
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/derandomized_smoothing/vision_transformers/pytorch.py
imports: from typing import Optional
  import torch
  from timm.models.vision_transformer import VisionTransformer
label_desc: Use image node_id 1 to create a new PatchEmbed object from inherited base classes: torch.nn with example: obj = PatchEmbed(patch_size, in_channels, embed_dim)