code: def __init__(self, config):
      super().__init__(config)
      self.num_labels = config.num_labels
      self.transformer = GPT2Model(config)
      self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
  
      self.init_weights()
  
      # Model parallel
      self.model_parallel = False
      self.device_map = None
  
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 GPT2ForSequenceClassification object from inherited base classes: GPT2PreTrainedModel with example: obj = GPT2ForSequenceClassification(config)