class_docstr: Vision transformer as per https://arxiv.org/abs/2010.11929.
class_docstr_tok: ["Vision","transformer","as","per","https",":","\/\/arxiv.org\/abs\/2010.11929","."]
code: def forward(self, x):
      # (n, c, h, w) -> (n, hidden_d, n_h, n_w)
      x = self.stem(x)
      # (n, hidden_d, n_h, n_w) -> (n, hidden_d, (n_h * n_w))
      x = x.reshape(x.size(0), x.size(1), -1)
      # (n, hidden_d, (n_h * n_w)) -> ((n_h * n_w), n, hidden_d)
      x = x.permute(2, 0, 1)
      if self.class_token is not None:
          # Expand the class token to the full batch
          class_token = self.class_token.expand(-1, x.size(1), -1)
          x = torch.cat([class_token, x], dim=0)
      x = x + self.pos_embedding
      x = self.encoder(x)
      # `token` or `pooled` features for classification
      x = x[0, :, :] if self.class_token is not None else x.mean(dim=0)
      return self.head(x)
  
imports: import math
  import numpy
  import torch
  from pycls.core.config import cfg
  from pycls.models.blocks import activation, conv2d, conv2d_cx, layernorm, layernorm_cx, linear, linear_cx, MultiheadAttention, norm2d, norm2d_cx, patchify2d, patchify2d_cx
  from torch.nn import init, Module, Parameter
label_desc: Use image node_id 4 for calling the ViT obj's underlying member method code with example usage: obj.forward(x) and returns: self