code: def __init__(
      self,
      config,
      num_classes,
      num_tokentypes=2,
      pre_process=True,
      post_process=True,
  ):
      super().__init__(
          config=config, share_embeddings_and_output_weights=False
      )
      args = get_args()
  
      self.num_classes = num_classes
      self.pre_process = pre_process
      self.post_process = post_process
  
      (
          self.language_model,
          self._language_model_key,
      ) = get_language_model(
          config=config,
          num_tokentypes=num_tokentypes,
          add_pooler=True,
          encoder_attn_mask_type=AttnMaskType.padding,
          pre_process=self.pre_process,
          post_process=self.post_process,
      )
  
      # Multi-choice head.
      if self.post_process:
          self.classification_dropout = torch.nn.Dropout(
              args.hidden_dropout
          )
          self.classification_head = get_linear_layer(
              args.hidden_size, self.num_classes, init_method
          )
          self._classification_head_key = "classification_head"
  
file_path: megatron-lm/megatron/model/classification.py
imports: import torch
  from megatron import get_args, print_rank_last
  from megatron.model.enums import AttnMaskType
  from megatron.model.bert_model import bert_extended_attention_mask, bert_position_ids
  from megatron.model.language_model import get_language_model
  from megatron.model.utils import get_linear_layer
  from megatron.model.utils import init_method_normal
  from megatron.model.utils import scaled_init_method_normal
  from .module import MegatronModule
label_desc: Use image node_id 1 to create a new Classification object from inherited base classes: MegatronModule with example: obj = Classification(config, num_classes, num_tokentypes, pre_process, post_process)