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
      # used patch_size x patch_size, stride patch_size to do linear projection
      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 = paddle.create_parameter(
          (width,), "float32"
      )
  
      self.positional_embedding = paddle.create_parameter(
          ((input_resolution // patch_size) ** 2 + 1, width), "float32"
      )
  
      self.ln_pre = nn.LayerNorm(width)
  
      self.transformer = Transformer(width, layers, heads)
  
      self.ln_post = nn.LayerNorm(width)
      self.proj = paddle.create_parameter(
          (width, output_dim), "float32"
      )
  
imports: from typing import Tuple
  from typing import Union
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import nn
  from .layers import AttentionPool2d
  from .layers import Bottleneck
  from .layers import MultiHeadAttention
  from .layers import ResidualAttentionBlock
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)