class_docstr: Transformer for DRL.
class_docstr_tok: ["Transformer","for","DRL","."]
code: def __init__(self, width: int, layers: int, heads: int):
      super().__init__()
      self.width = width
      self.layers = layers
      self.resblocks = nn.Sequential(
          *[ResidualAttentionBlock(width, heads) for _ in range(layers)]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  import torch
  from torch import nn
  from towhee.models.drl.until_module import LayerNorm
  from collections import OrderedDict
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)