class_docstr: ViT transformer
  ViT Transformer, classifier is a single Linear layer for finetune,
  For training from scratch, two layer mlp should be used.
  Classification is done using cls_token.
  Args:
      image_size: int, input image size, default: 224
      patch_size: int, patch size, default: 16
      in_channels: int, input image channels, default: 3
      num_classes: int, number of classes for classification, default: 1000
      embed_dim: int, embedding dimension (patch embed out dim), default: 768
      depth: int, number ot transformer blocks, default: 12
      num_heads: int, number of attention heads, default: 12
      mlp_ratio: float, ratio of mlp hidden dim to embed dim(mlp in dim), default: 4.0
      qkv_bias: bool, If True, enable qkv(nn.Linear) layer with bias, default: True
      dropout: float, dropout rate for linear layers, default: 0.
      attention_dropout: float, dropout rate for attention layers default: 0.
      droppath: float, droppath rate for droppath layers, default: 0.
code: def forward(self, x):
      x = self.patch_embedding(x)
      x = self.encoder(x)
      logits = self.classifier(
          x[:, 0]
      )  # take only cls_token as classifier
      return logits
  
imports: import copy
  import paddle
  import paddle.nn
  from disco_diffusion_ernievil_base.vit_b_16x.ernievil2.transformers.droppath import DropPath
label_desc: Use image node_id 3 for calling the VisualTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: logits