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=False,
      )
  
      scale = width**-0.5
      self.class_embedding = nn.Parameter(scale * torch.randn(width))
      self.positional_embedding = nn.Parameter(
          scale
          * torch.randn(
              (input_resolution // patch_size) ** 2 + 1, width
          )
      )
      self.ln_pre = LayerNorm(width)
  
      self.transformer = Transformer(width, layers, heads)
  
      self.ln_post = LayerNorm(width)
      self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new VisionTransformer object from inherited base classes: nn with example: obj = VisionTransformer(input_resolution, patch_size, width, layers, heads, output_dim)