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 = paddle.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 = self.create_parameter(
          shape=(width,), default_initializer=zeros_
      )
      self.add_parameter("class_embedding", self.class_embedding)
  
      self.positional_embedding = self.create_parameter(
          shape=((input_resolution // patch_size) ** 2 + 1, width),
          default_initializer=zeros_,
      )
      self.add_parameter(
          "positional_embedding", self.positional_embedding
      )
      self.ln_pre = paddle.nn.LayerNorm(width)
      self.transformer = Transformer(width, layers, heads)
      self.ln_post = paddle.nn.LayerNorm(width)
      self.proj = self.create_parameter(
          shape=(width, output_dim), default_initializer=zeros_
      )
      self.add_parameter("proj", self.proj)
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import paddle
  from paddle.nn.initializer import Constant
  from paddle.vision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
label_desc: Use image node_id 1 to create a new VisionTransformer object from inherited base classes: paddle.nn with example: obj = VisionTransformer(input_resolution, patch_size, width, layers, heads, output_dim)