code: def __init__(self, idx, hidden_size, cfg_attention, use_bias=True):
      super().__init__()
      self.self_attention = get_attention_mechanism(
          idx, hidden_size, cfg_attention
      )
      if cfg_attention.skip_output_projection:
          self.dense = torch.nn.Identity()
      else:
          self.dense = torch.nn.Linear(
              self.self_attention.output_dim, hidden_size, bias=use_bias
          )
  
      self.LAYOUT = self.self_attention.LAYOUT
  
imports: import torch
  from transformers import PretrainedConfig, PreTrainedModel
  from transformers import AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification
  from typing import Optional
  from omegaconf import OmegaConf
  from .fff import FFF
  from .components import _get_norm_fn, _get_nonlin_fn, EmbeddingComponent, PoolingComponent, PredictionHeadComponent, GLU, get_extended_attention_mask, _init_module
  from .attention import get_attention_mechanism
label_desc: Use image node_id 1 to create a new AttentionComponent object from inherited base classes: torch.nn with example: obj = AttentionComponent(idx, hidden_size, cfg_attention, use_bias)