code: def __init__(
      self,
      width: int,
      layers: int,
      heads: int,
      attn_mask: torch.Tensor = None,
  ):
      super().__init__()
      self.width = width
      self.layers = layers
      self.resblocks = nn.Sequential(
          *[
              ResidualAttentionBlock(width, heads, attn_mask)
              for _ in range(layers)
          ]
      )
  
imports: from collections import OrderedDict
  import timm
  from torch import nn
  from lavis.models.ulip_models import losses
  from torch.nn.parameter import Parameter
  from easydict import EasyDict
  import torch
  import numpy
  from lavis.common.dist_utils import download_cached_file
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(width, layers, heads, attn_mask)