class_docstr: A wrapper class for doing masked language modeling with Mega
class_docstr_tok: ["A","wrapper","class","for","doing","masked","language","modeling","with","Mega"]
code: def __init__(self, mega_args, depth, vocab_size):
      super().__init__()
      self.mega = MegaLM(mega_args, depth, vocab_size)
      self.mlm_head = nn.Linear(mega_args.encoder_embed_dim, vocab_size)
      self.dropout = nn.Dropout(p=0.1)
  
file_path: transformers/src/transformers/models/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import os
  import pickle
  import torch
  from torch import nn
  from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM
label_desc: Use image node_id 1 to create a new OriginalMegaForMaskedLM object from inherited base classes: nn with example: obj = OriginalMegaForMaskedLM(mega_args, depth, vocab_size)