class_docstr: Vision Transformer with support for patch input
      
code: def forward(self, x):
      x = self.patch_embed(x)
      x_shape = paddle.shape(x)  # b * c * h * w
  
      cls_tokens = self.cls_token.expand((x_shape[0], -1, -1))
      x = x.flatten(2).transpose([0, 2, 1])  # b * hw * c
      x = paddle.concat([cls_tokens, x], axis=1)
  
      if paddle.shape(x)[1] == self.pos_embed.shape[1]:
          x = x + self.pos_embed
      else:
          x = x + self.resize_pos_embed(
              self.pos_embed, (self.pos_h, self.pos_w), x_shape[2:]
          )
      x = self.pos_drop(x)
  
      res = []
      for idx, blk in enumerate(self.blocks):
          x = blk(x)
          if self.final_norm and idx == len(self.blocks) - 1:
              x = self.norm(x)
          res.append(x[:, 1:, :])
  
      return res, x_shape
  
imports: import os
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils, logger
  from paddleseg.models.backbones.transformer_utils import to_2tuple, DropPath, Identity
label_desc: Use image node_id 4 for calling the VisionTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: res,  x_shape