code: def __init__(self, config):
      super().__init__(config)
      self.num_labels = config.num_labels
      self.transformer = GPTNeoModel(config)
      self.score = nn.Linear(
          config.hidden_size, self.num_labels, bias=False
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: import os
  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_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_gpt_neo import GPTNeoConfig
label_desc: Use image node_id 1 to create a new GPTNeoForSequenceClassification object from inherited base classes: GPTNeoPreTrainedModel with example: obj = GPTNeoForSequenceClassification(config)