code: def get_data_collator(args: "Args", pad_token_id: int):
      """Pad input_ids to the right, create labels by setting the padding tokens to -100, and
      create attention_mask to ignore the padding tokens"""
  
      def collate(
          examples: list[dict[str, list[int]]]
      ) -> dict[str, torch.Tensor]:
          input_ids_unpadded = [
              example["input_ids"] for example in examples
          ]
          labels_unpadded = [example["labels"] for example in examples]
          padding_length = (
              args.max_training_seq_length
              if args.pad_to_max_length
              else None
          )
          input_ids = pad_sequences(
              input_ids_unpadded,
              pad_token_id,
              "right",
              padding_length=padding_length,
          )
          labels = pad_sequences(
              labels_unpadded,
              IGNORED_INDEX,
              "right",
              padding_length=padding_length,
          )
  
          assert input_ids.shape == labels.shape
          assert len(input_ids) == len(examples)
          # Enforced in `map_raw_dataset`
          assert input_ids.shape[-1] <= args.max_training_seq_length
          if args.pad_to_max_length:
              assert input_ids.shape[-1] == args.max_training_seq_length
  
          return {
              "input_ids": input_ids,
              "labels": labels,
              "attention_mask": input_ids.ne(pad_token_id),
          }
  
      return collate
  
file_path: magicoder/experiments/train_comment_code_pairs.py
imports: import random
  from dataclasses import dataclass, field
  from typing import cast
  import torch
  from datasets import Dataset, load_dataset
  from tqdm.auto import tqdm
  from transformers import HfArgumentParser, Trainer, TrainingArguments
  from magicoder.llm_wrapper import DecodingConfig, EncodingConfig, TokenizationContext, get_model_context, pad_sequences
  from magicoder.prompt_template import MAGICODER_PROMPT
  from magicoder.utils import N_CORES, read_jsonl, write_jsonl
label_desc: Use image node_id 4 for calling a global function with example usage: get_data_collator(args, pad_token_id) and returns: collate, dict