hunk.code_window: @register_model
   def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs):
       """ ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token
       """
       model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
       model = _create_vision_transformer_relpos('vit_relpos_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
       return model
   @register_model
  
hunk.labels: keep keep keep keep keep replace keep keep keep keep keep
hunk.after_edit:     model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, **kwargs)
  
hunk.file_path: timm/models/vision_transformer_relpos.py
hunk.type: replace
file: """ Normalization layers and wrappers
  """
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class GroupNorm(nn.GroupNorm):
      def __init__(self, num_channels, num_groups=32, eps=1e-5, affine=True):
          # NOTE num_channels is swapped to first arg for consistency in swapping norm layers with BN
          super().__init__(num_groups, num_channels, eps=eps, affine=affine)
  
      def forward(self, x):
          return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
  
  
  class LayerNorm2d(nn.LayerNorm):
      """ LayerNorm for channels of '2D' spatial BCHW tensors """
      def __init__(self, num_channels):
          super().__init__(num_channels)
  
      def forward(self, x: torch.Tensor) -> torch.Tensor:
          return F.layer_norm(
              x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
  