code: def __init__(self, config):
      super().__init__()
      hidden_size = config.hidden_size
      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 = GPT2Attention(config)
      self.ln_2 = nn.LayerNorm(
          hidden_size, eps=config.layer_norm_epsilon
      )
  
      if config.add_cross_attention:
          self.crossattention = GPT2Attention(
              config, is_cross_attention=True
          )
          self.ln_cross_attn = nn.LayerNorm(
              hidden_size, eps=config.layer_norm_epsilon
          )
  
      self.mlp = GPT2MLP(inner_dim, config)
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  from .configuration_gpt2 import GPT2Config
  from fastNLP.transformers.torch.activations import ACT2FN
  from fastNLP.transformers.torch.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from fastNLP.transformers.torch.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from fastNLP.transformers.torch.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from fastNLP.transformers.torch.utils.model_parallel_utils import assert_device_map, get_device_map
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP.core.log import logger
label_desc: Use image node_id 1 to create a new GPT2Block object from inherited base classes: Module with example: obj = GPT2Block(config)