class_docstr: Vision Transformer with support for patch or hybrid CNN input stage
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","or","hybrid","CNN","input","stage"]
code: def forward(self, x):
      x = self.patch_embed(x)
      x = x.flatten(2)
      x = paddle.transpose(x, perm=[0, 2, 1])
      x = self.blocks(x)
      x = x.mean(1)
  
      x = paddle.reshape(x, [-1, self.embed_dim[-1]])
      if self.distillation:
          x = self.head(x), self.head_dist(x)
          if not self.training:
              x = (x[0] + x[1]) / 2
      else:
          x = self.head(x)
      return x
  
imports: import itertools
  import math
  import warnings
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Constant
  from paddle.nn.initializer import TruncatedNormal
  from paddle.regularizer import L2Decay
  from .vision_transformer import Identity
  from .vision_transformer import ones_
  from .vision_transformer import trunc_normal_
  from .vision_transformer import zeros_
label_desc: Use image node_id 2 for calling the LeViT obj's underlying member method code with example usage: obj.forward(x) and returns: x