code: def __init__(
      self,
      input_resolution: int,
      patch_size: int,
      width: int,
      layers: int,
      heads: int,
      output_dim: int,
  ):
      super().__init__()
      self.input_resolution = input_resolution
      self.output_dim = output_dim
      self.conv1 = nn.Conv2D(
          in_channels=3,
          out_channels=width,
          kernel_size=patch_size,
          stride=patch_size,
          bias_attr=False,
      )
  
      scale = width**-0.5
      # self.class_embedding = nn.Parameter(scale * paddle.randn(width))
      class_embedding = self.create_parameter([width])
      self.add_parameter("class_embedding", class_embedding)
      # self.positional_embedding = nn.Parameter(scale * paddle.randn([(input_resolution // patch_size) ** 2 + 1, width)])
      positional_embedding = self.create_parameter(
          [(input_resolution // patch_size) ** 2 + 1, width]
      )
      self.add_parameter("positional_embedding", positional_embedding)
      self.ln_pre = nn.LayerNorm(width)
  
      self.transformer = Transformer(width, layers, heads)
  
      self.ln_post = nn.LayerNorm(width)
      # self.proj = nn.Parameter(scale * paddle.randn([width, output_dim]))
      proj = self.create_parameter([width, output_dim])
      self.add_parameter("proj", proj)
  
imports: from collections import OrderedDict
  from typing import Tuple
  from typing import Union
  import numpy
  import paddle
  import paddle.nn.functional
  from disco_diffusion_cnclip_vitb16.cn_clip.clip import _tokenizer
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.configuration_bert import BertConfig
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.modeling_bert import BertModel
  from paddle import nn
  from paddle.nn import MultiHeadAttention
label_desc: Use image node_id 1 to create a new VisualTransformer object from inherited base classes: nn with example: obj = VisualTransformer(input_resolution, patch_size, width, layers, heads, output_dim)