code: def __init__(self, config: BioGptConfig):
      super().__init__()
      self.embed_dim = config.hidden_size
  
      self.self_attn = BioGptAttention(
          embed_dim=self.embed_dim,
          num_heads=config.num_attention_heads,
          dropout=config.attention_probs_dropout_prob,
          is_decoder=True,
      )
      self.dropout = config.hidden_dropout_prob
      self.activation_fn = ACT2FN[config.hidden_act]
      self.activation_dropout = config.activation_dropout
  
      self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
  
      self.fc1 = nn.Linear(self.embed_dim, config.intermediate_size)
      self.fc2 = nn.Linear(config.intermediate_size, self.embed_dim)
      self.final_layer_norm = nn.LayerNorm(self.embed_dim)
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_biogpt import BioGptConfig
label_desc: Use image node_id 1 to create a new BioGptDecoderLayer object from inherited base classes: nn with example: obj = BioGptDecoderLayer(config)