code: def __init__(
      self,
      width: int,
      layers: int,
      heads: int,
      attn_mask: paddle.Tensor = None,
  ):
      super().__init__()
      self.width = width
      self.layers = layers
      self.resblocks = paddle.nn.Sequential(
          *[
              ResidualAttentionBlock(width, heads, attn_mask)
              for _ in range(layers)
          ]
      )
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import paddle
  from paddle.nn.initializer import Constant
  from paddle.vision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: paddle.nn with example: obj = Transformer(width, layers, heads, attn_mask)