code: def __init__(
      self,
      input_resolution: int,
      patch_size: int,
      width: int,
      layers: int,
      heads: int,
      use_grad_checkpointing: bool,
  ):
      super().__init__()
      self.input_resolution = input_resolution
      self.num_features = width
      self.num_heads = heads
      self.num_patches = (input_resolution // patch_size) ** 2
      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(self.num_patches + 1, width)
      )
      self.ln_pre = LayerNorm(width)
  
      self.transformer = Transformer(
          width,
          layers,
          heads,
          use_grad_checkpointing=use_grad_checkpointing,
      )
  
imports: from collections import OrderedDict
  from itertools import repeat
  import collections.abc
  import math
  import torch
  import torch.nn.functional
  from torch import nn
  from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
  from lavis.models.eva_vit import convert_weights_to_fp16
  from lavis.common.dist_utils import download_cached_file
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, use_grad_checkpointing)