code: def __init__(self, n_ctx, config, scale=False):
      super().__init__()
      hidden_size = config.n_embd
      inner_dim = (
          config.n_inner
          if config.n_inner is not None
          else 4 * hidden_size
      )
      self.ln_1 = nn.LayerNorm(
          hidden_size, eps=config.layer_norm_epsilon
      )
      self.attn = Attention(hidden_size, n_ctx, config, scale)
      self.ln_2 = nn.LayerNorm(
          hidden_size, eps=config.layer_norm_epsilon
      )
      # self.adapter_ln = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
      if config.add_cross_attention:
          self.crossattention = Attention(
              hidden_size, n_ctx, config, scale, is_cross_attention=True
          )
          self.ln_cross_attn = nn.LayerNorm(
              hidden_size, eps=config.layer_norm_epsilon
          )
      self.mlp = MLP(inner_dim, config)
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(n_ctx, config, scale)