code: def forward(self, model_input, attention_mask, tokentype_ids=None):
      extended_attention_mask = bert_extended_attention_mask(
          attention_mask
      )
      input_ids = model_input
      position_ids = bert_position_ids(input_ids)
  
      lm_output = self.language_model(
          input_ids,
          position_ids,
          extended_attention_mask,
          tokentype_ids=tokentype_ids,
      )
  
      if self.post_process:
          _, pooled_output = lm_output
          classification_output = self.classification_dropout(
              pooled_output
          )
          classification_logits = self.classification_head(
              classification_output
          )
  
          # Reshape back to separate choices.
          classification_logits = classification_logits.view(
              -1, self.num_classes
          )
  
          return classification_logits
      return lm_output
  
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 3 for calling the Classification obj's underlying member method code with example usage: obj.forward(model_input, attention_mask, tokentype_ids) and returns: lm_output, classification_logits