code: def __init__(self, config: OPTConfig):
      super().__init__()
      self.embed_dim = config.hidden_size
      self.self_attn = OPTAttention(
          embed_dim=self.embed_dim,
          num_heads=config.num_attention_heads,
          dropout=config.attention_dropout,
          is_decoder=True,
      )
      self.do_layer_norm_before = config.do_layer_norm_before
      self.dropout = config.dropout
      self.activation_fn = ACT2FN[config.activation_function]
  
      self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
      self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
      self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
      self.final_layer_norm = nn.LayerNorm(self.embed_dim)
  
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.opt.configuration_opt import OPTConfig
label_desc: Use image node_id 1 to create a new OPTDecoderLayer object from inherited base classes: nn with example: obj = OPTDecoderLayer(config)