class_docstr: A small language model based on the design of GPT-2 using nn.Sequential
  for compatibility with Pipe
code: def __init__(
      self, ntokens, ninp, nhead, nhid, dropout, initrange, ndecoder
  ):
      layers = [
          EmbeddingLayer(ntokens, ninp, initrange),
          PositionalEncodingLayer(ninp, dropout),
      ]
      for _ in range(ndecoder):
          layers.append(
              TransformerDecoderLayer(ninp, nhead, nhid, dropout)
          )
  
      layers.append(LinearLayer(ninp, ntokens, initrange))
      super().__init__(*layers)
  
imports: import argparse
  import math
  import os
  import time
  import torch
  import torch.nn
  from benchmark_dataset import BenchmarkLMDataset, collate_sentences_lm
  from torch.distributed import rpc
  from torch.distributed.pipeline.sync import Pipe
  from torch.distributed.pipeline.sync.utils import partition_model
  from torch.optim import Adam
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new TransformerLMSequential object from inherited base classes: nn with example: obj = TransformerLMSequential(ntokens, ninp, nhead, nhid, dropout, initrange, ndecoder)