code: def __init__(
      self,
      context_length,
      vocab_size,
      width,
      layers: int = 12,
      heads: int = 8,
      drop_path: float = 0.0,
      autogressive: bool = True,
  ):
      super().__init__()
  
      self.token_embedding = nn.Embedding(vocab_size, width)
  
      self.context_length = context_length
      self.positional_embedding = nn.Parameter(
          torch.empty(self.context_length, width)
      )
  
      self.width = width
      self.layers = layers
      self.autogressive = autogressive
      attn_mask = self.build_attention_mask() if autogressive else None
      dpr = [
          x.item() for x in torch.linspace(0, drop_path, layers)
      ]  # stochastic depth decay rule
      self.resblocks = nn.ModuleList(
          [
              ResidualAttentionBlock(width, heads, attn_mask, dpr[i])
              for i in range(layers)
          ]
      )
  
      self.ln_final = LayerNorm(width)
  
imports: import os
  from collections import OrderedDict
  import torch
  from mmcv.cnn.bricks import DropPath
  from torch import nn
  from transformers import CLIPTokenizer
  from .utils import get_prompt_templates
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(context_length, vocab_size, width, layers, heads, drop_path, autogressive)