code: def __init__(self, config):
      super().__init__(config)
      self.num_labels = config.num_labels
      self.transformer = CTRLModel(config)
      self.classifier = nn.Linear(
          config.n_embd, self.num_labels, bias=False
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_ctrl import CTRLConfig
label_desc: Use image node_id 1 to create a new CTRLForSequenceClassification object from inherited base classes: CTRLPreTrainedModel with example: obj = CTRLForSequenceClassification(config)