diff --git a/timm/models/davit.py b/timm/models/davit.py
index 74d51f73..94c7c8dd 100644
--- a/timm/models/davit.py
+++ b/timm/models/davit.py
@@ -34,6 +34,7 @@ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 __all__ = ['DaViT']
 
+
 # modified nn.Sequential that includes a size tuple in the forward function
 # FIXME doesn't work with torchscript/JIT
 # Module 'SequentialWithSize' has no attribute '_modules'
@@ -43,6 +44,7 @@ class SequentialWithSize(nn.Sequential):
             x, size = module(x, size)
         return x, size
 
+
 class ConvPosEnc(nn.Module):
     def __init__(self, dim : int, k : int=3, act : bool=False, normtype : str='none'):
 
@@ -190,7 +192,9 @@ class ChannelBlock(nn.Module):
 
 
     def forward(self, x : Tensor, size: Tuple[int, int]):
+
         x = self.cpe1(x, size)
+        
         cur = self.norm1(x)
         cur = self.attn(cur)
         x = x + self.drop_path(cur)
@@ -520,7 +524,6 @@ class DaViT(nn.Module):
         self.norms = norm_layer(self.num_features)
         self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
         self.apply(self._init_weights)
-       
     
     def _init_weights(self, m):
         if isinstance(m, nn.Linear):
@@ -551,7 +554,6 @@ class DaViT(nn.Module):
         size: Tuple[int, int] = (x.size(2), x.size(3))
         x, size = self.stages(x, size)
         
-        # take final feature and norm
         x = self.norms(x)
         H, W = size
         x = x.view(-1, H, W, self.embed_dims[-1]).permute(0, 3, 1, 2).contiguous()
@@ -567,7 +569,6 @@ class DaViT(nn.Module):
         
     def forward(self, x):
         return self.forward_classifier(x)
-        
 
 
 def checkpoint_filter_fn(state_dict, model):
@@ -577,6 +578,7 @@ def checkpoint_filter_fn(state_dict, model):
     
     if 'state_dict' in state_dict:
         state_dict = state_dict['state_dict']
+
     import re
     out_dict = {}
     for k, v in state_dict.items():
@@ -590,6 +592,7 @@ def checkpoint_filter_fn(state_dict, model):
     
 
 def _create_davit(variant, pretrained=False, **kwargs):
+
     default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
     out_indices = kwargs.pop('out_indices', default_out_indices)
     model = build_model_with_cfg(
@@ -599,7 +602,6 @@ def _create_davit(variant, pretrained=False, **kwargs):
         pretrained_filter_fn=checkpoint_filter_fn,
         feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
         **kwargs)
-
     return model