code: def forward(
      self, input_ids, attention_masks, tokentype_ids, lm_labels
  ):
      # inputs of the forward function
      # input_ids: [batch_size, sub_seq_len]
      # attention_mask: [batch_size, seq_len]
      # tokentype_ids: [batch_size, sub_seq_len]
      # outputs of preprocessor
      # pos_ids: [batch_size, sub_seq_len]
      # attention_masks: [batch_size, 1, sub_seq_len, seq_len]
      pos_ids, attention_masks = self.preprocessor(
          input_ids, attention_masks
      )
  
      hidden_states = self.embedding(input_ids, pos_ids, tokentype_ids)
  
      # hidden_states shape change:
      # [batch_size, sub_seq_len, hidden_size] -> [sub_seq_len, batch_size, hidden_size]
      hidden_states = hidden_states.transpose(0, 1).contiguous()
  
      for idx, layer in enumerate(self.bert_layers):
          hidden_states = layer(hidden_states, attention_masks)
  
      hidden_states = hidden_states.transpose(0, 1).contiguous()
      output = self.layer_norm(hidden_states)
  
      # hidden_states: [sub_seq_len, batch_size, hidden_size]
      # word_embedding: [vocab_size, hidden_size]
      return self.head(
          output, self.embedding.word_embedding_weight, lm_labels
      )
  
imports: import inspect
  import torch
  import torch.nn
  from colossalai.kernel import LayerNorm
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.context.parallel_mode import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer.wrapper import PipelineSharedModuleWrapper
  from colossalai.legacy.pipeline.utils import partition_uniform
  from colossalai.logging import get_dist_logger
  from .layers import BertDualHead, BertLayer, Embedding, PreProcessor, VocabEmbedding
  from .layers.init_method import init_normal, output_init_normal
label_desc: Use image node_id 5 for calling the BertForPretrain obj's underlying member method code with example usage: obj.forward(input_ids, attention_masks, tokentype_ids, lm_labels) and returns: self