code: def __init__(self, config):
      super().__init__(config)
      self.num_labels = config.num_labels
      self.num_layers = config.num_hidden_layers
  
      self.bert = DeeBertModel(config)
      self.dropout = nn.Dropout(config.hidden_dropout_prob)
      self.classifier = nn.Linear(
          config.hidden_size, self.config.num_labels
      )
  
      self.init_weights()
  
imports: import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.bert.modeling_bert import BERT_INPUTS_DOCSTRING, BERT_START_DOCSTRING, BertEmbeddings, BertLayer, BertPooler, BertPreTrainedModel
label_desc: Use image node_id 1 to create a new DeeBertForSequenceClassification object from inherited base classes: BertPreTrainedModel with example: obj = DeeBertForSequenceClassification(config)