class_docstr: Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
  are not all of the same length.
  
  Args:
      tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
          The tokenizer used for encoding the data.
      mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
          The probability with which to (randomly) mask tokens in the input.
  
  .. note::
  
      For best performance, this data collator should be used with a dataset having items that are dictionaries or
      BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
      :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
      argument :obj:`return_special_tokens_mask=True`.
class_docstr_tok: ["Data","collator","used","for","language","modeling",".","Inputs","are","dynamically","padded","to","the","maximum","length","of","a","batch","if","they","are","not","all","of","the","same","length",".","Args",":","tokenizer","(",":","class",":","`","~transformers.PreTrainedTokenizer","`","or",":","class",":","`","~transformers.PreTrainedTokenizerFast","`",")",":","The","tokenizer","used","for","encoding","the","data",".","mlm_probability","(",":","obj",":","`","float","`",",","`","optional","`",",","defaults","to","0.15",")",":","The","probability","with","which","to","(","randomly",")","mask","tokens","in","the","input",".","..","note",":",":","For","best","performance",",","this","data","collator","should","be","used","with","a","dataset","having","items","that","are","dictionaries","or","BatchEncoding",",","with","the",":","obj",":","`","``","special_tokens_mask","''","`","key",",","as","returned","by","a",":","class",":","`","~transformers.PreTrainedTokenizer","`","or","a",":","class",":","`","~transformers.PreTrainedTokenizerFast","`","with","the","argument",":","obj",":","`","return_special_tokens_mask=True","`","."]
code: def __post_init__(self):
      if self.tokenizer.mask_token is None:
          raise ValueError(
              "This tokenizer does not have a mask token which is necessary for masked language modeling. "
              "You should pass `mlm=False` to train on causal language modeling instead."
          )
  
imports: import logging
  import os
  import sys
  import time
  from collections import defaultdict
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import datasets
  import flax
  import jax
  import jax.numpy
  import numpy
  import optax
  from datasets import load_dataset
  from flax import jax_utils, traverse_util
  from flax.training import train_state
  from flax.training.common_utils import get_metrics, onehot, shard
  from tqdm import tqdm
  from transformers import CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoTokenizer, FlaxAutoModelForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed
label_desc: Use image node_id 1 for calling the FlaxDataCollatorForLanguageModeling obj's underlying member method code with example usage: obj.__post_init__() without return types