class_docstr: Vision Transformer with support for patch input
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","input"]
code: def forward_features(self, x):
      # B = x.shape[0]
      B = paddle.shape(x)[0]
      x = self.patch_embed(x)
      class_embedding = self.class_embedding.expand((B, -1, -1))
      x = paddle.concat((class_embedding, x), axis=1)
      x = x + self.positional_embedding
      x = self.pos_drop(x)
      x = self.norm_pre(x)
      for blk in self.blocks:
          x = blk(x)
  
      # x = self.norm_post(x[:, 0, :])
      x = self.norm_post(x)
      # x = self.classfy(x)
      return x
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import Layer
  from paddle.nn import Linear
  from paddle.nn.initializer import Constant
  from paddle.nn.initializer import Normal
  from paddle.nn.initializer import TruncatedNormal
label_desc: Use image node_id 3 for calling the VisionTransformer obj's underlying member method code with example usage: obj.forward_features(x) and returns: x