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 __repr__(self):
      return (
          f"{self.__class__.__name__}("
          f"N-batch={len(self)}, "
          f"batch_size={self.batch_size}, "
          f"key_file={self.key_file}, "
      )
  
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 2 for calling the UnsortedBatchSampler obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, len, str, self, str, self, str