code: def generate_random_sequences(self, seq_len=20, batch_size=2):
      source_seq = torch.randint(
          high=self.d_vocab, size=(seq_len, batch_size)
      )
      target_seq = torch.randint(
          high=self.d_vocab, size=(seq_len, batch_size)
      )
  
      return source_seq, target_seq
  
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 2 for calling the TransformerWithLMHead obj's underlying member method code with example usage: obj.generate_random_sequences(seq_len, batch_size) and returns: source_seq,  target_seq