code: def __init__(
      self,
      width: int,
      layers: int,
      heads: int,
      attn_mask: torch.Tensor = None,
      use_grad_checkpointing=False,
  ):
      super().__init__()
      self.width = width
      self.layers = layers
      self.resblocks = nn.Sequential(
          *[
              ResidualAttentionBlock(
                  width,
                  heads,
                  attn_mask,
                  use_grad_checkpointing and i > 12,
              )
              for i in range(layers)
          ]
      )
  
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 Transformer object from inherited base classes: nn with example: obj = Transformer(width, layers, heads, attn_mask, use_grad_checkpointing)