code: def create_data():
      # create Hugging Face tokenizer
      tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
      vocab_size = tokenizer.vocab_size
      sep_id = tokenizer.encode(
          tokenizer.special_tokens_map["sep_token"],
          add_special_tokens=False,
      )[0]
  
      # source text to id
      src_text = [
          "What is the fastest library in the world?",
          "You are so pretty!",
          "What do you love me for?",
          "The sparrow outside the window hovering on the telephone pole.",
      ]
      src_tokens = tokenizer.batch_encode_plus(
          src_text, padding=True, return_tensors="pt"
      )
      src_tokens = src_tokens["input_ids"].to(torch.device("cuda:0"))
      batch_size, src_seq_len = src_tokens.size(0), src_tokens.size(1)
  
      # target text to id
      trg_text = [
          "I guess it must be LightSeq, because ByteDance is the fastest.",
          "Thanks very much and you are pretty too.",
          "Love your beauty, smart, virtuous and kind.",
          "You said all this is very summery.",
      ]
      trg_tokens = tokenizer.batch_encode_plus(
          trg_text, padding=True, return_tensors="pt"
      )
      trg_tokens = trg_tokens["input_ids"].to(torch.device("cuda:0"))
      trg_seq_len = trg_tokens.size(1)
  
      # left shift 1 token as the target output
      target = trg_tokens.clone()[:, 1:]
      trg_tokens = trg_tokens[:, :-1]
  
      return (
          tokenizer,
          src_text,
          src_tokens,
          trg_text,
          trg_tokens,
          target,
          sep_id,
          vocab_size,
          batch_size,
          src_seq_len,
          trg_seq_len,
      )
  
file_path: lightseq/examples/training/custom/run.py
imports: import argparse
  import torch
  from transformers import BertTokenizer
  from lightseq.training import LSTransformer, LSCrossEntropyLayer, LSAdam
  from lightseq.training.ops.pytorch.quantization import disable_quant, enable_quant
label_desc: Use image node_id 1 for calling a global function with example usage: create_data() and returns: tokenizer,  src_text,  src_tokens,  trg_text,  trg_tokens,  target,  sep_id,  vocab_size,  batch_size,  src_seq_len,  trg_seq_len