code: def __init__(self, d_vocab=100, d_model=16):
      super().__init__()
      self.d_vocab = d_vocab
      self.d_model = d_model
  
      self.embed_tokens = nn.Embedding(d_vocab, d_model)
  
      self.transformer = TransformerWithCustomOutput(
          d_model,
          num_encoder_layers=2,
          num_decoder_layers=2,
          dim_feedforward=64,
      )
  
      self.output_proj = nn.Linear(d_model, d_vocab)
  
imports: from collections import OrderedDict
  from dataclasses import dataclass
  import tempfile
  import unittest
  import torch
  from torch import nn
  from fairscale.fair_dev.testing.testing import dist_init
  from fairscale.nn import FullyShardedDataParallel
  from fairscale.nn import auto_wrap, enable_wrap
label_desc: Use image node_id 1 to create a new TransformerWithLMHead object from inherited base classes: nn with example: obj = TransformerWithLMHead(d_vocab, d_model)