class_docstr: BatchSampler with constant batch-size.
  
  Any sorting is not done in this class,
  so no length information is required,
  This class is convenient for decoding mode,
  or not seq2seq learning e.g. classification.
  
  Args:
      batch_size:
      key_file:
class_docstr_tok: ["BatchSampler","with","constant","batch-size",".","Any","sorting","is","not","done","in","this","class",",","so","no","length","information","is","required",",","This","class","is","convenient","for","decoding","mode",",","or","not","seq2seq","learning","e.g",".","classification",".","Args",":","batch_size",":","key_file",":"]
code: def __iter__(self) -> Iterator[Tuple[str, ...]]:
      return iter(self.batch_list)
  
file_path: espnet/espnet2/samplers/unsorted_batch_sampler.py
imports: import logging
  from typing import Iterator, Tuple
  from typeguard import check_argument_types
  from espnet2.fileio.read_text import read_2columns_text
  from espnet2.samplers.abs_sampler import AbsSampler
label_desc: Use image node_id 4 for calling the UnsortedBatchSampler obj's underlying member method code with example usage: obj.__iter__() and returns: iter