class_docstr: Vision Transformer
  A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
      https://arxiv.org/abs/2010.11929
class_docstr_tok: ["Vision","Transformer","A","PyTorch","impl","of",":","`","An","Image","is","Worth","16x16","Words",":","Transformers","for","Image","Recognition","at","Scale","`","-","https",":","\/\/arxiv.org\/abs\/2010.11929"]
code: def forward_features(self, x):
      out = []
      x = self.patch_embed1(x)
      x = self.pos_drop(x)
      for i, blk in enumerate(self.blocks1):
          if self.use_checkpoint and i < self.checkpoint_num[0]:
              x = checkpoint.checkpoint(blk, x)
          else:
              x = blk(x)
      x_out = self.norm1(x.permute(0, 2, 3, 1))
      out.append(x_out.permute(0, 3, 1, 2).contiguous())
      x = self.patch_embed2(x)
      for i, blk in enumerate(self.blocks2):
          if self.use_checkpoint and i < self.checkpoint_num[1]:
              x = checkpoint.checkpoint(blk, x)
          else:
              x = blk(x)
      x_out = self.norm2(x.permute(0, 2, 3, 1))
      out.append(x_out.permute(0, 3, 1, 2).contiguous())
      x = self.patch_embed3(x)
      for i, blk in enumerate(self.blocks3):
          if self.use_checkpoint and i < self.checkpoint_num[2]:
              x = checkpoint.checkpoint(blk, x)
          else:
              x = blk(x)
      x_out = self.norm3(x.permute(0, 2, 3, 1))
      out.append(x_out.permute(0, 3, 1, 2).contiguous())
      x = self.patch_embed4(x)
      for i, blk in enumerate(self.blocks4):
          if self.use_checkpoint and i < self.checkpoint_num[3]:
              x = checkpoint.checkpoint(blk, x)
          else:
              x = blk(x)
      x_out = self.norm4(x.permute(0, 2, 3, 1))
      out.append(x_out.permute(0, 3, 1, 2).contiguous())
      return tuple(out)
  
imports: from collections import OrderedDict
  import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  import numpy
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from annotator.uniformer.mmcv_custom import load_checkpoint
  from annotator.uniformer.mmseg.utils import get_root_logger
  from ..builder import BACKBONES
label_desc: Use image node_id 7 for calling the UniFormer obj's underlying member method code with example usage: obj.forward_features(x) and returns: tuple